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Energy and cost analysis of bottom waste cooking oil (B-WCO) based solid alcohol biofuel and supernatant waste cooking oil (S-WCO) based biodiesel

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Heliyon 2025年第4期11卷 e42180页

作者： Nabizadeh, Ramin García, I.L. Bahmani, Zohreh Norouzian Baghani, Abbas Sadjadi, Sodeh Yaghmaeian, Kamyar Kamarehie, Bahram Mahvi, Amir Hossein Yunesian, Masud Center for Solid Waste Research Institute for Environmental Research Tehran University of Medical Sciences Tehran Iran Department of Environmental Health Engineering School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Physical Chemistry and Applied Thermodynamics Edf. Leonardo da Vinci Campus de Rabanales Universidad de Córdoba Campus de Excelencia Internacional Agroalimentario ceiA3 Spain Environmental health research center Lorestan university of medical sciences Khorramabad Iran Radiation Application Research School Nuclear Science and Technology Research Institute Tehran Iran Department of Research Methodology and Data Analysis Institute for Environmental Research (IER) Tehran University of Medical Sciences Tehran Iran

Waste cooking oil (WCO) represents one of the most available renewable energy sources to produce solid alcohol biofuel (SABF) and biodiesel. The objective of this work is to conduct energy and cost balances for biodiesel and SABF generation from supernatant waste cooking oil (S-WCO) and bottom waste cooking oil (B-WCO) derived from catering facilities in Iran, respectively. The total energy input/output for SABF and biodiesel, respectively, were 31.738/37.180 MJ/L and 34.269/42.163 MJ/L, with the majority of the energy contribution being B-WCO/S-WCO (78.77/73.57 %), followed by machinery (19.76/18.30 %), and ethanol/methanol (0.69/7.47 %), in both cases. Renewable energy and non-renewable energy inputs for SABF and biodiesel were calculated as 25.071 MJ/L/6.667 MJ/L and 25.280 MJ/L/8.990 MJ/L of total energy input, respectively. Total cost and revenues for SABF vs. biodiesel production processes were 1.318 vs. 1.342 $/L and 5.620 vs. 2.481$/L, respectively. Gross return, net return and beneﬁt to cost ratio values for SABF vs. biodiesel were 1.449 vs. 1.548 $/L, 1.060 vs. 1.157 $/L, and 1.827 vs. 1.876, respectively. Hence, considering the energy balances of the valorization process for WCO, the population growth in cities, and the need for inexpensive and plentiful sources of renewable energy, the production of SABF and biodiesel may offer a valuable solution in developing countries. © 2025 The Authors

关键词： Biodiesel Cost analyses Energy balance Solid alcohol biofuel Waste cooking oil

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Crack Catcher ai — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher ai uses novel smart fracture mechanics approach with Artificial Intelligence (ai) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging

arXiv

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arXiv 2022年

作者： Jia, Haozhe Bai, Chao Cai, Weidong Huang, Heng Xia, Yong Research & Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'An710072 China School of Computer Science University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States JD Finance America Corporation California CA94043 United States

In our previous work, i.e., HNF-Net, high-resolution feature representation and light-weight non-local self-attention mechanism are exploited for brain tumor segmentation using multi-modal MR imaging. In this paper, we extend our HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic discrimination enhancing blocks to further exploit global semantic discrimination for the obtained high-resolution features. We trained and evaluated our HNF-Netv2 on the multi-modal Brain Tumor Segmentation Challenge (BraTS) 2021 dataset. The result on the test set shows that our HNF-Netv2 achieved the average Dice scores of 0.878514, 0.872985, and 0.924919, as well as the Hausdorff distances (95%) of 8.9184, 16.2530, and 4.4895 for the enhancing tumor, tumor core, and whole tumor, respectively. Our method won the RSNA 2021 Brain Tumor ai Challenge Prize (Segmentation Task), which ranks 8th out of all 1250 submitted results. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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HOW DOES THE BRaiN COMPUTE WITH PROBABILITIES?

arXiv

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arXiv 2024年

作者： Haefner, Ralf M. Beck, Jeff Savin, Cristina Salmasi, Mehrdad Pitkow, Xaq Department of Brain and Cognitive Sciences University of Rochester RochesterNY United States Department of Neurobiology Duke University DurhamNC United States Departments of Neural Science and Data Science New York University New YorkNY United States Gatsby Computational Neuroscience Unit Max Planck UCL Centre for Computational Psychiatry and Ageing Research University College London United Kingdom Neuroscience Institute Department of Machine Learning Carnegie Mellon University PittsburghPA United States Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Electrical and Computer Engineering Department of Computer Science Rice University HoustonTX United States

This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question 'How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations - Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) - and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and *** Codes 92 Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

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Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

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Towards Cross-device and Training-free Robotic Grasping in 3D Open World

arXiv

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arXiv 2024年

作者： Zhao, Weiguang Jiang, Chenru Zhang, Chengrui Sun, Jie Yan, Yuyao Zhang, Rui Huang, Kaizhu Department of Computer Science University of Liverpool LiverpoolL69 7ZX United Kingdom Data Science Research Center Duke Kunshan University Suzhou215316 China Department of Electrical Engineering University of Liverpool LiverpoolL69 7ZX United Kingdom Department of Mechatronics and Robotics Xi’an-Jiaotong Liverpool University Suzhou215123 China School of Robotic Xi’an Jiaotong-Liverpool University Suzhou215123 China Department of Foundational Mathematics Xi’an Jiaotong-Liverpool University Suzhou215123 China

— Robotic grasping in the open world is a critical component of manufacturing and automation processes. While numerous existing approaches depend on 2D segmentation output to facilitate the grasping procedure, accurately determining depth from 2D imagery remains a challenge, often leading to limited performance in complex stacking scenarios. In contrast, techniques utilizing 3D point cloud data inherently capture depth information, thus enabling adeptly navigating and manipulating a diverse range of complex stacking scenes. However, such efforts are considerably hindered by the variance in data capture devices and the unstructured nature of the data, which limits their generalizability. Consequently, much research is narrowly concentrated on managing designated objects within specific settings, which confines their real-world applicability. This paper presents a novel pipeline capable of executing object grasping tasks in open-world scenarios even on previously unseen objects without the necessity for training. Additionally, our pipeline supports the flexible use of different 3D point cloud segmentation models across a variety of scenes. Leveraging the segmentation results, we propose to engage a training-free binary clustering algorithm that not only improves segmentation precision but also possesses the capability to cluster and localize unseen objects for executing grasping operations. In our experiments, we investigate a range of open-world scenarios, and the outcomes underscore the remarkable robustness and generalizability of our pipeline, consistent across various environments, robots, cameras, and objects. The code will be made available upon acceptance of the paper. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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A Tutorial on Movable Antennas for Wireless Networks

arXiv

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arXiv 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Chengdu611731 China National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electronic and Information Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada State Key Laboratory of CNS/ATM MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

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Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for aiding Diagnosis of Blood Diseases

arXiv

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arXiv 2024年

作者： Chen, Yifei Zhang, Chenyan Chen, Ben Huang, Yiyu Sun, Yifei Wang, Changmiao Fu, Xianjun Dai, Yuxing Qin, Feiwei Peng, Yong Gao, Yu HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou310018 China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen518172 China School of Artificial Intelligence Zhejiang College of Security Technology Wenzhou325016 China National-local Joint Engineering Research Center for Digitalized Electrical Design Technology Wenzhou University Wenzhou325035 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Department of Hematology Zhejiang Hospital Hangzhou310018 China

In standard hospital blood tests, the traditional process requires doctors to manually isolate leukocytes from microscopic images of patients’ blood using microscopes. These isolated leukocytes are then categorized via automatic leukocyte classifiers to determine the proportion and volume of different types of leukocytes present in the blood samples, aiding disease diagnosis. This methodology is not only time-consuming and labor-intensive, but it also has a high propensity for errors due to factors such as image quality and environmental conditions, which could potentially lead to incorrect subsequent classifications and misdiagnosis. Contemporary leukocyte detection methods exhibit limitations in dealing with images with fewer leukocyte features and the disparity in scale among different leukocytes, leading to unsatisfactory results in most instances. To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR). To tackle the issue of leukocyte scale disparity, we designed the High-level Screening-feature Fusion Pyramid (HS-FPN), enabling multi-level fusion. This model uses high-level features as weights to filter low-level feature information via a channel attention module and then merges the screened information with the high-level features, thus enhancing the model’s feature expression capability. Further, we address the issue of leukocyte feature scarcity by incorporating a multi-scale deformable self-attention module in the encoder and using the self-attention and cross-deformable attention mechanisms in the decoder, which aids in the extraction of the global features of the leukocyte feature maps. The effectiveness, superiority, and generalizability of the proposed MFDS-DETR method are confirmed through comparisons with other cutting-edge leukocyte detection models using the private WBCDD, public LISC and BCCD datasets. Our source code and private WBCCD

关键词： Object detection

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Understanding the predictability of path flow distribution in urban road networks using an information entropy approach

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Multimodal Transportation 2024年第2期3卷

作者： Guo, Bao Huang, Zhiren Zheng, Zhihao Zhang, Fan Wang, Pu School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan Province Central South University Changsha China Shandong Provincial Communications Planning and Design Institute Group Co. Ltd Jinan China Department of Computer Science Aalto University Helsinki Finland Department of Civil Engineering McGill University Montreal H3A 0C3 QC Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved. © 2024

关键词： Human mobility Information entropy Path flow distribution Predictability

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引用

Neuroscience Bulletin 2022年第10期38卷 1215-1228页

作者： Liwei Sun Wenjing Zhang Mengyue Wang Songjian Wang Zhen Li Cui Zhao Meng Lin Qian Si Xia Li Ying Liang Jing Wei Xu Zhang Renji Chen Chunlin Li School of Biomedical Engineering Capital Medical UniversityBeijing100069China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Capital Medical UniversityBeijing100069China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application School of Biomedical EngineeringCapital Medical UniversityBeijing100069China Beijing Stomatological Hospital Capital Medical UniversityBeijing100050China School of Life Science Beijing Institute of TechnologyBeijing100081China Beijing Institute of Otolaryngology-Head and Neck Surgery Beijing100005China Key Laboratory of Otolaryngology-Head and Neck Surgery(Capital Medical University) Ministry of EducationBeijing100005China Beijing Tongren Hospital Capital Medical UniversityBeijing100005China Department of Ultrasound Beijing Obstetrics and Gynecology HospitalCapital Medical UniversityBeijing100026China Peking University First Hospital Beijing100034China

Cleft lip and/or palate(CLP)are the most common craniofacial malformations in *** problems often persist even after cleft repair,such that follow-up articulation training is usually ***,the neural mechanism behind effective articulation training remains largely *** used fMRI to investigate the differences in brain activation,functional connectivity,and effective connectivity across CLP patients with and without articulation training and matched normal *** found that training promoted task-related brain activation among the articulation-related brain networks,as well as the global attributes and nodal efficiency in the functional-connectivity-based graph of the *** results reveal the neural correlates of effective articulation training in CLP patients,and this could contribute to the future improvement of the post-repair articulation training program.

关键词： Non-syndromic cleft lip and palate Articulation training fMRI Functional connectivity Graph-based network analysis

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