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Spleen-preserving distal pancreatectomy from multi-port to reducedport surgery approach

World Journal of Gastrointestinal Surgery 2023年第7期15卷 1501-1511页

作者： Ching-Lung Hsieh Tung-Sheng Tsai Cheng-Ming Peng Teng-Chieh Cheng Yi-Jui Liu Department of Computer Science and Information Engineering Feng Chia UniversityTaichung 40724TaiwanChina Department of Surgery Chung Shan Medical University HospitalTaichung 40201TaiwanChina PhD Program of Electrical and Communications Engineering Feng Chia UniversityTaichung 40724Taiwan School of Medicine Chung Shan Medical UniversityTaichung 40201Taiwan Da Vinci Minimally Invasive Surgery Center Chung Shan Medical University HospitalTaichung 40201Taiwan Department of Automatic Control Engineering Feng Chia UniversityTaichung 407Taiwan

BACKGROUND Minimally invasive pancreatic surgery via the multi-port approach has become a primary surgical method for distal pancreatectomy(DP)due to its advantages of lower wound pain and superior cosmetic *** studies have applied reduced-port techniques for DP in an attempt to enhance cosmetic outcomes due to the minimally invasive *** recent review studies have compared multi-port laparoscopic DP(LDP)and multi-port robotic DP(RDP);most of these studies concluded multi-port RDP is more beneficial than multi-port LDP for spleen ***,there have been no comprehensive reviews of the value of reduced-port LDP and reduced-port *** To search for and review the studies on spleen preservation and the clinical outcomes of minimally invasive DP that compared reduced-port DP surgery with multi-port DP *** The PubMed medical database was searched for articles published between 2013 and *** search terms were implemented using the following Boolean search algorithm:(“distal pancreatectomy”OR“left pancreatectomy”OR“peripheral pancreatic resection”)AND(“reduced-port”OR“single-site”OR“single-port”OR“dual-incision”OR“single-incision”)AND(“spleen-preserving”OR“spleen preservation”OR“splenic preservation”).A literature review was conducted to identify studies that compared the perioperative outcomes of reduced-port LDP and reduced-port *** Fifteen articles published in the period from 2013 to 2022 were retrieved using three groups of search *** studies were added after manually searching the related ***,10 papers were selected after removing case reports(n=3),non-English language papers(n=1),technique papers(n=1),reviews(n=1),and animal studies(n=1).The common items were defined as items reported in more than five papers,and data on these common items were extracted from all *** ten studies included a total of 337 patients(females/males:231/106)who underwent *** total,166 patients(females/male

关键词： Minimally invasive surgery Robotic distal pancreatectomy Laparoscopic distal pancreatectomy Spleen preservation Reduced-port Multi-port

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Reconfigurable Intelligent Surface-Aided Joint Radar and Covert Communications: Fundamentals, Optimization, and Challenges

arXiv

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arXiv 2022年

作者： Du, Hongyang Kang, Jiawen Niyato, Dusit Zhang, Jiayi Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Future wireless communication systems will evolve toward multi-functional integrated systems to improve spectrum utilization and reduce equipment sizes. A joint radar and communication (JRC) system, which can support simultaneous information transmission and target detection, has been regarded as a promising solution for emerging applications such as autonomous vehicles. In JRC, data security and privacy protection are critical issues. Thus, we first apply covert communication into JRC and propose a joint radar and covert communication (JRCC) system to achieve high spectrum utilization and secure data transmission simultaneously. In the JRCC system, an existence of sensitive data transmission is hidden from a maliciously observant warden. However, the performance of JRCC is restricted by severe signal propagation environment and hardware devices. Fortunately, reconfigurable intelligent surfaces (RISs) can change the signal propagation smartly to improve the networks performance with low cost. We first overview fundamental concepts of JRCC and RIS and then propose the RIS-aided JRCC system design. Furthermore, both covert communication and radar performance metrics are investigated and a game theory-based covert rate optimization scheme is designed to achieve secure communication. Finally, we present several promising applications and future directions of RIS-aided JRCC systems. © 2022, CC BY.

关键词： Data transfer

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A Distributed Feature Selection Approach Over Hadoop for Accurate Lung Cancer Classification Based on Grasshopper Algorithm and Rough Sets

SSRN

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SSRN 2023年

作者： Song, Xu Zhou, Hongyu Teo, Brian Sheng Xian Abo-Alnadr, Ayman Hamed, Ahmed School of Computer and Information Engineering Anyang Normal University Anyang455000 China School of Software Engineering Anyang Normal University Anyang455000 China School of Graduate Studies Shah Alam40100 Malaysia Kafrelsheikh University Egypt Department of Computer Science Faculty of Computers and Information Damanhour University Damanhour22511 Egypt

In the specialized field of lung cancer research, precise feature selection has become paramount, especially given the extensive and intricate datasets available. Many of these datasets house a plethora of features, of which a substantial number may be redundant, leading to potential inaccuracies and increased computational demands. Although the Rough Set (RS) and Multigranular Rough Set (MGRS) models have demonstrated efficacy in feature selection, their computational complexities can be limiting. To address this, we introduce an innovative solution, integrating the MGRS with the Grasshopper Optimization Algorithm (GOA)—a metaheuristic technique derived from grasshopper foraging behaviors. To manage the expansive lung cancer data, we employ the Hadoop framework for streamlined distributed processing. By distributing the enhanced GOA tasks within Hadoop, we aspire to efficiently process large-scale datasets. The proposed algorithm's efficacy is assessed using dedicated lung cancer datasets, benchmarked via classifiers such as Random Forest and K-Nearest Neighbor. Preliminary results highlight the superior performance of our approach compared to prevalent metaheuristic strategies, with the MGRS model enhancing performance notably when employed as an objective function. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Dynamic Spectrum Allocation for Bursty Traffic Using Quantum Annealing

Dynamic Spectrum Allocation for Bursty Traffic Using Quantum...

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International Conference on Platform Technology and Service (PlatCon)

作者： Shuta Inoue Yuusuke Kawakita Yoshito Tobe Graduate School of Science and Engineering Aoyama Gakuin University Sagamihara Japan Department of Information and Computer Sciences Kanagawa Institute of Technology Atsugi Japan Department of Integrated Information Aoyama Gakuin University Sagamihara Japan

As the demand for wireless communications continues to grow, securing limited spectrum resources has become an issue. Under such circumstances, Dynamic Spectrum Allocation (DSA), in which existing frequency bands are jointly used and allocated to carriers, has been attracting attention. However, the problem is that a large number of base stations and interferers causes a combinatorial explosion, which requires an enormous amount of computation time. In a previous study, this problem was viewed as a combinatorial optimization problem and formulated to be solved by quantum annealing for the time slot-specific requirements of each base station. However, each base station uses the frequency band differently, and the timeslot length requirement must also be considered. In this paper, we focus on the DSA problem of maximizing the satisfaction of the time-based requirements of each base station and describe a formulation for solving the problem using quantum annealing. These time-based requirements can accommodate the demand for bursty traffic, which was not the case in previous papers. We also present implementation results using Fixstars Amplify and discuss future prospects.

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Asynchronous Gathering Algorithms for Autonomous Mobile Robots with Lights 23rd

Asynchronous Gathering Algorithms for Autonomous Mobile Rob...

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23rd International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2021

作者： Nakai, Rikuo Sudo, Yuichi Wada, Koichi Graduate School of Science and Engineering Hosei University Koganei City Japan Faculty of Computer and Information Sciences Hosei University Koganei City Japan Faculty of Science and Engineering Hosei University Koganei City Japan

ISBN: (纸本)9783030910808

We consider a Gathering problem for n autonomous mobile robots with persistent memory called light in an asynchronous scheduler (ASYNC). Gathering is well known to be impossible when robots have no lights in basic standard models if the system is semi-synchronous (SSYNC) or even centralized (only one robot is active at each time). It is known that robots can solve Gathering with 10 colors of lights in ASYNC. This result is obtained by combining the following results. (1) The simulation of SSYNC robots with k colors by ASYNC robots with 5k colors [7], and (2) Gathering is solved by SSYNC robots with 2 colors [28]. In this paper, we improve the result by reducing the number of colors and show that Gathering can be solved by ASYNC robots with 3 colors of lights. We also show that we can construct a simulation algorithm of any unfair SSYNC algorithm using k colors by ASYNC robots with 3k colors, where unfairness does not guarantee that every robot is activated infinitely often. Combining this simulation and the Gathering algorithm by SSYNC robots with 2 colors [28], we obtain a Gathering algorithm by ASYNC robots with 6 colors. Our main result can be obtained by reducing the number of colors from 6 to 3. © 2021, Springer Nature Switzerland AG.

关键词： Color

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Identifying Audio-Visual Familiarity Effects on Emotion via Directed Functional Connectivity 6

Identifying Audio-Visual Familiarity Effects on Emotion via ...

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6th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2021

作者： Iddas, Hadriana Lahuddin, Harlinda Bahri, Syamsul Iramina, Keiji Graduate School of Systems Life Sciences Kyushu University Makassar Indonesia University of Muslim Indonesia Faculty of Computer Science Makassar Indonesia Kyushu University Graduate School of Systems Life Sciences Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9781728167145

Some studies have reported that audiovisual stimuli have a significant influence on generating human emotions. In this paper, we propose to adopt directed functional connectivity to identify how emotions, in terms of high valence (HV) and low valence (LV), are affected by familiar/unfamiliar music videos. The current study applied Directed Transfer Function (DTF) to estimate connectivity between each pair of channels, followed by theoretical graph analysis (e.g., degree and strength). Our results showed that watching familiar/unfamiliar musical videos had a significant impact on emotions. First, HV showed greater functional connectivity during familiar conditions, while LV revealed stronger functional connectivity for unfamiliar tasks within the alpha and beta bands. Second, in the beta band during unfamiliar conditions, increased outflows of LV were identified at the right central, and enhanced inflows at the left frontal. © 2021 IEEE.

关键词： Transfer functions

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Accelerate Inference of CNN Models on CPU via Column Combining Based on Simulated Annealing

Accelerate Inference of CNN Models on CPU via Column Combini...

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International Symposium on Computing and Networking (CANDAR)

作者： Chien-Hung Lin Ding-Yong Hong Pangfeng Liu Jan-Jan Wu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan The Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan The Graduate School of Advanced Technology National Taiwan University Taipei Taiwan

Convolutional Neural Networks (CNNs) have been successful in various computer vision tasks. However, the size of state-of-the-art CNN models tends to be tremendous, which results in very long inference times and high memory usage. Model compression technology such as unstructured pruning can prune a significant proportion of parameters without affecting accuracy, but efficient utilization of sparsity remains a challenge. Column combining compress unstructurally-pruned CNN models by combining multiple sparse columns in a convolutional filter matrix into a single dense column. In addition, pruning all but the largest magnitude weight in each row of the combined column further compresses the matrix effectively. However, previous work did not address the details of partitioning sparse columns to minimize the negative impact of additional pruning on the performance of the model. In this work, we first prove that the column partition problem is an NP-Complete problem. Next, we propose a column combining scheme based on simulated annealing and global unstructured pruning to minimize the adverse effects of additional pruning on model performance. We implement the acceleration of column-combined CNN models using the TVM AI compiler without special hardware support. The proposed scheme achieves more efficient model compression, leading to a 0.65% improvement in accuracy and a $1.24\times$ faster inference time on VGG19 under 88% sparsity with the TinyImageNet dataset.

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Performance LoRa Technology for Autonomous Vehicles 24

Performance LoRa Technology for Autonomous Vehicles

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Adi, Puput Dani Prasetyo Purnama, Iwan Siregar, Arfanda Anugrah Juledi, Angga Putra Edi, Firman Karim, Abdul Wahyu, Yuyu Maulana, Fairuz Iqbal Susilo, Suhardi Atmoko Budi Toni Harahap, Ilham Maliki Patria, Lintang Bandung Indonesia Labuhanbatu University Faculty of Science and Technology North Sumatra Labuhanbatu Indonesia Politeknik Negeri Medan Telecommunication Engineering Study Program North Sumatra Medan City Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Riau Islands Batam City Indonesia Budi Darma University Department of Information Technology North Sumatra Medan Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia University Labuhanbatu Faculty of Teacher Training and Education North Sumatra Labuhanbatu Indonesia Universitas Terbuka Faculty of Engineering Jakarta Indonesia

ISBN: (纸本)9798350313956

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss. © 2023 IEEE.

关键词： Autonomous vehicles

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Blockchain based Public Auditing Outsourcing for Cloud Storage 27

Blockchain based Public Auditing Outsourcing for Cloud Stora...

引用

27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Lin, Yangfei Li, Jie Kimura, Shigetomo Zhang, Yongbing Ji, Yusheng Yang, Yang Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki Tsukuba Japan Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Tsukuba Faculty of Engineering Information and Systems Ibaraki Tsukuba Japan National Institute of Informatics Information Systems Architecture Research Division Tokyo Japan School of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (纸本)9781665408783

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user. © 2021 IEEE.

关键词： Smart contract

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