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A framework to find flow disruptions in the operating rooms' consumable supplies

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Perioperative Care and Operating Room Management 2020年 21卷 100136-100136页

作者： Hosseinzadeh, Fatemeh Sepehri, Mohammad Mehdi Khasha, Roghaye Taherkhani, Nasrin Master of Science in Industrial Engineering Faculty of Industrial and Systems Engineering Tarbiat Modares University Tehran *** Iran Professor of Healthcare Systems Engineering Faculty of Industrial and Systems Engineering Tarbiat Modares University Tehran *** Iran Research Scholar Center of Excellence in Healthcare Systems Engineering Tarbiat Modares University Tehran *** Iran Faculty member of Computer Engineering Payam-e-Noor University Saveh *** Iran

Background: Operating room consumables play a very vital role in the surgery process. There are some disruptions in their flow that can affect the whole process of surgery. Finding these disruptions is the first step to eliminate their negative effects. Although previous studies have examined workflow disruptions in the operating rooms, there are few studies focusing specifically on the operating room consumables' workflow and examining the process thoroughly with a framework. Methods: This study introduces a framework to find disruptions that directly or indirectly may affect the flow of operating room consumables. The framework consists of four steps: 1) mapping the operating room consumables flow, 2) finding disruptions in terms of operating room pauses due to shortage of items and categorizing them, 3) classifying the disruptions and finding their sources, and 4) taking these disruptions as risk factors and rating them using risk management approach. To achieve this objective, 20 surgical operations (mainly kidney and general surgeries) were observed at two hospitals in Tehran over a five-month period from May 10 until October 10, 2018. By reviewing the literature and field observations, 11 disruptions were extracted and then assessed using the supply chain risk score. Results: We applied the proposed framework to the two studied hospitals and categorized the disruptions in the flow of operating room consumables in three different categories: human factors, policies, and external factors. After prioritizing the disruptions, "inappropriate recording points" related to inventory control issues was found to be the most effective and frequent issue that affected consumables flow. Conclusions: Well understanding the disruptions in the flow of consumables is very important because finding their sources are the first step to eliminate or decrease them. This paper presents a framework to identify the disruptions in the flow of operating room consumables, which can be fu

关键词： Consumables supply chain Disruption Material management Operating rooms Risk management

Accelerating adoption of species-agnostic plant sensors for precision farming

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Nature Reviews Electrical Engineering

Nature Reviews Electrical engineering 2025年 58-70页

作者： Abdullah Bukhamsin Khaled Nabil Salama Jürgen Kosel Ikram Blilou Bioengineering Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Center of Excellence — Sustainable Food Security (SFS) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Sensor Systems Division Silicon Austria Labs Villach Austria Plant Science Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Precision farming is an optimized management farming scheme that seeks to link the real-time needs of crops with the nutrients to be administered. Sensing platforms that can monitor the physiological status of crops in situ are key to enabling timely and localized interventions. However, the underdevelopment of plant sensing strategies limits the potential of precision farming. In this Review, we discuss the challenges and advancements in phyto-monitoring, focusing on strategies that are applicable to a wide range of plant species and suitable for field deployment. We explore species-agnostic sensors, including optical and electrochemical sensors, whose operation is based on principles that are widely applicable to all plant species. These platforms enable real-time monitoring of the physiological state of crops by assessing key biomarkers, such as plant hormones, and metabolites such as salicylic acid and reactive oxygen species. Evaluating these systems, we conclude that an integrative sensing approach is necessary to compensate for the limitations of the individual methods and can provide a holistic view of crop health. Cost-effective species-agnostic sensors are thus needed to provide information that can be used to minimize the resource footprint of farming and meet the growing global demand.

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Layerless Additive Manufacturing of Metal Alloy Lattices Using Immiscible-Interface Assisted Direct Metal Drawing

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Procedia Manufacturing 2019年 34卷 647-654页

作者： Li He Fan Fei Wenbo Wang Xuan Song Department of Industrial and Systems Engineering University of Iowa Iowa City IA 52242 USA Center for Computer Aided Design University of Iowa Iowa City IA 52242 USA

State-of-the-art metal alloy additive manufacturing (AM) techniques construct a three-dimensional structure through sintering or melting dry metal powders in a layer-by-layer fashion, which typically results in several AM-specific issues in the final structure, such as staircase effect, residual stress and highly-orientated microstructures. In this paper, we present a new extrusion-based metal AM process, named Immiscible-interface assisted Direct Metal Drawing (II-DMD), which fabricates metal alloy structures, in particular lattice structures, in a layerless manner. In the II-DMD process, metal lattice structures are fabricated by continuously extruding a metal colloidal suspension within a second immiscible matrix colloidal suspension. The shape of the metal colloidal suspension is stabilized due to the presence of an immiscible interface between the two colloidal suspension systems. Dense metal lattice structures can be achieved via post-consolidation of the self-stabilized metal-matrix systems, including liquid-phase drying and metal-phase sintering. The II-DMD process is presented and the immiscible-interface-assisted self-stabilization mechanism is studied. The post-consolidation processes are discussed. Several test cases were fabricated and characterized.

关键词： immiscible interface direct metal drawing colloidal suspension self-stabilization sintering

Meta auxiliary learning for facial action unit detection

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arXiv 2021年

作者： Li, Yong Shan, Shiguang Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Cas Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Cas Center for Excellence in Brain Science and Intelligence Technology

Despite the success of deep neural networks on facial action unit (AU) detection, better performance depends on a large number of training images with accurate AU annotations. However, labeling AU is time-consuming, expensive, and error-prone. Considering AU detection and facial expression recognition (FER) are two highly correlated tasks, and facial expression (FE) is relatively easy to annotate, we consider learning AU detection and FER in a multi-task manner. However, the performance of the AU detection task cannot be always enhanced due to the negative transfer in the multi-task scenario. To alleviate this issue, we propose a Meta Auxiliary Learning method (MAL) that automatically selects highly related FE samples by learning adaptative weights for the training FE samples in a meta learning manner. The learned sample weights alleviate the negative transfer from two aspects: 1) balance the loss of each task automatically, and 2) suppress the weights of FE samples that have large uncertainties. Experimental results on several popular AU datasets demonstrate MAL consistently improves the AU detection performance compared with the state-of-the-art multi-task and auxiliary learning methods. MAL automatically estimates adaptive weights for the auxiliary FE samples according to their semantic relevance with the primary AU detection task. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Graph jigsaw learning for cartoon face recognition

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arXiv 2021年

作者： Li, Yong Lao, Lingjie Cui, Zhen Shan, Shiguang Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognize cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner. Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually. GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time. Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements. © 2021, CC BY-NC-SA.

关键词： Face recognition

Learning fair face representation with progressive cross transformer

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arXiv 2021年

作者： Li, Yong Sun, Yufei Cui, Zhen Shan, Shiguang Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Face recognition (FR) has made extraordinary progress owing to the advancement of deep convolutional neural networks. However, demographic bias among different racial cohorts still challenges the practical face recognition system. The race factor has been proven to be a dilemma for fair FR (FFR) as the subject-related specific attributes induce the classification bias whilst carrying some useful cues for FR. To mitigate racial bias and meantime preserve robust FR, we abstract face identityrelated representation as a signal denoising problem and propose a progressive cross transformer (PCT) method for fair face recognition. Originating from the signal decomposition theory, we attempt to decouple face representation into i) identity-related components and ii) noisy/identity-unrelated components induced by race. As an extension of signal subspace decomposition, we formulate face decoupling as a generalized functional expression model to cross-predict face identity and race information. The face expression model is further concretized by designing dual cross-transformers to distill identity-related components and suppress racial noises. In order to refine face representation, we take a progressive face decoupling way to learn identity/racespecific transformations, so that identity-unrelated components induced by race could be better disentangled. We evaluate the proposed PCT on the public fair face recognition benchmarks (BFW, RFW) and verify that PCT is capable of mitigating bias in face recognition while achieving state-of-the-art FR performance. Besides, visualization results also show that the attention maps in PCT can well reveal the race-related/biased facial regions. © 2021, CC BY-NC-SA.

关键词： Face recognition

Recent progress of integrated circuits and optoelectronic chips

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Science China(Information Sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic photonic neural computing

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Authors' Reply

IEEE Sensors Journal 2018年第3期18卷 1336-1337页

作者： Ghanbari, Milad Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering University of Tehran Tehran*** Iran Control and Intelligent Processing Center of Excellence Advanced Control Systems Laboratory College of Engineering School of Electrical and Computer Engineering University of Tehran Tehran*** Iran

The experimental results of the paper titled 'Delay compensation of tilt sensors based on mems accelerometer using data fusion technique' was commented by Bodnicki and Luczak. In this note, we address the raised comments by the mentioned authors. Moreover, appropriate explanations are provided in order to make the procedure of the experimental section more clear. © 2001-2012 IEEE.

关键词： Kalman filters

Rectification and bolometric terahertz radiation detectors based on perforated graphene structures exhibiting plasmonic resonant response

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arXiv 2025年

作者： Ryzhii, V. Tang, C. Ryzhii, M. Otsuji, T. Shur, M.S. Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Frontier Research Institute for Interdisciplinary Sciences Tohoku University Sendai980-8578 Japan School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Center of Excellence ENSEMBLE3 Warsaw01-919 Poland International Research Institute of Disaster Science Tohoku University Sendai980-0845 Japan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Electronics of the Future Inc. ViennaVA22181-6117 United States

We propose and evaluate the characteristics of the terahertz (THz) detectors based on perforated graphene layers (PGLs). The PGL structures constitute the interdigital in-plane arrays of the graphene microribbons (GMRs) connected by the sets of narrow constrictions, which form the graphene nanoribbon (GNR) bridges. The PGL detector operation is associated with the rectification and hot-carrier bolometric mechanisms. The excitation of plasmonic oscillations in the GMRs can reinforce these mechanisms. . The room temperature PGL detector responsivity and detectivity are calculated as function of the radiation frequency and device structure parameters. The effects of the rectification and hot-carrier mechanisms are compared. The PGL THz detectors under consideration can exhibit highly competitive values of responsivity and detectivity. © 2025, CC BY.

关键词： Terahertz wave detectors