检索结果-内蒙古大学图书馆

Biochar Incorporation Enhances Growth, Yield, and Water Productivity of Transplanted Rice under Alternate Wetting and Drying Irrigation

引用

Water Conservation Science and engineering 2025年第2期10卷 1-13页

作者： Dhakal, Subesh Ahmed, Sheikh Faruk Sarkar, Deepranjan Ullah, Hayat Himanshu, Sushil Kumar Zulfiqar, Farhad Cha-um, Suriyan Datta, Avishek Agricultural Systems and Engineering Department of Food Agriculture and Natural Resources School of Environment Resources and Development Asian Institute of Technology Klong Luang Thailand Department of Crop Botany Gazipur Agricultural University Gazipur Bangladesh Agribusiness Management Department of Food Agriculture and Natural Resources School of Environment Resources and Development Asian Institute of Technology Klong Luang Thailand National Center for Genetic Engineering and Biotechnology National Science and Technology Development Agency Klong Luang Thailand

Sustainable rice production under changing climate faces several challenges, including deteriorating soil fertility and water scarcity, resulting in low productivity. To address these issues, improved and appropriate crop management approaches are fundamental. Biochar as an organic amendment and alternate wetting and drying (AWD) irrigation as a water-saving technology hold immense potential in this regard. The current study aimed at investigating the impact of biochar on growth, physiological traits, yield, and water productivity of transplanted rice under AWD irrigation. A factorial experiment consisting of five biochar doses (0, 5, 10, 15, and 20 t ha−1) and three soil water potential levels (0, − 15, and − 30 kPa) maintained through AWD irrigation was conducted. Incremental biochar doses improved crop performance, and the best results were observed at 20 t ha−1. The performance of rice (growth, physiological traits, and yield) was largely similar between 0 (sufficient soil moisture availability) and − 15 kPa soil water potential levels; however, − 30 kPa significantly damaged rice as it reduced leaf relative water content by 21%, membrane stability index by 13%, and net photosynthetic rate by 11%, ultimately reducing grain yield by 10% in comparison to 0 kPa. Conversely, − 15 kPa did not show any notable abnormality, rather this soil water potential level was beneficial for some tested parameters. Interactively, biochar application at 20 t ha−1 and AWD irrigation at − 15 kPa demonstrated the overall best crop performance in terms of yield enhancement (~ 25%) and water productivity improvement (~ 70%). The inclusion of biochar in a fertilizer management program and AWD irrigation as a water-saving technology is recommended for transplanted rice cultivation to curtail the detrimental impacts of depleting soil fertility and water-deficit stress.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

引用

arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Development of Machine Learning-Based Assessment System for Laparoscopic Surgical Skills Using Motion-Capture

Development of Machine Learning-Based Assessment System for ...

引用

IEEE/SICE International Symposium on System Integration

作者： Koki Ebina Takashige Abe Lingbo Yan Kiyohiko Hotta Madoka Highuchi Naoya Iwahara Jun Furumido Masafumi Kon Sachiyo Murai Yo Kurashima Shunsuke Komizunai Teppei Tsujita Kazuya Sase Xiaoshuai Chen Taku Senoo Nobuo Shinohara Atsushi Konno Graduate School of Information Science and Technology Hokkaido University Sapporo Japan Department of Renal and Genitourinary surgery Faculty of Medicine Hokkaido University Sapporo Japan Clinical simulation center Faculty of Medicine Hokkaido University Sapporo Japan Faculty of Engineering and Design Kagawa University Takamatsu Japan Department of Mechanical Engineering National Defense Academy of Japan Yokosuka Japan Department of Mechanical Engineering and Intelligent Systems Tohoku Gakuin University Sendai Japan Graduate School of Science and Technology Hirosaki University Hi-rosaki Japan

Laparoscopic surgery is a widely used surgical technique, on the other hand, its high degree of difficulty makes it difficult for beginners to learn the technique efficiently. In addition, recent working hour restrictions and shortages of surgeons have resulted in insufficient training time, and establishing the efficiently training methods is becoming urgent needs. Therefore, to promote the skill proficiency of novice surgeons, machine learning-based assessment system for laparo-scopic surgical skills was developed. A measurement system with a simple configuration was introduced so that trainees can easily use it alone. Alternatively, the indices related to the opening ratio and the rotation angle of surgical instruments, which were measured in the authors' previous study were no longer available. Therefore, comparative experiments were conducted to verify the effect of the lack of indices related to these data on the accuracy of skill evaluation. Based on the measurement data of 104 wet lab trainings measured in the previous study, a machine learning model that evaluates surgeon's skill at 3 levels based on the number of surgical experiences, and global operative assessment of laparoscopic skills (GOALS) which is a type of surgical skill evaluation index were established. By using the explainable AI method, this system can present the skill evaluation result including its basis to the trainee. Since the developed system can be easily operated by a GUI-based program, trainees can confirm the quantitative evaluation result on-site immediately after the training.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Digital Forensic Investigation Framework for Marine Industry

Digital Forensic Investigation Framework for Marine Industry

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Vihara Fernando Amila Senarathne Department of Computer Systems Engineering Faculty of Graduate Studies and Research Sri Lanka Institute of Information Technology Malabe Sri Lanka

A massive 400% increase of cyber-attacks was reported in the Marine industry since February 2020 up to date. Experiments and research are being conducted towards safeguarding data in various technological aspects of the marine environment with regards to navigation, transportation and system management. At present, generic investigation processes are developed for computer incidents and forensic readiness in this sector, but those do not describe the investigation process precisely. Although there are incident analysis technologies implemented for the Marine environment, it lacks reliable digital forensic tools for evidence extraction and analysis. Therefore, in this research, a comprehensive digital forensic investigation framework is proposed, answering the issues of existing frameworks, and a tool is implemented for the examination of navigational evidence. Forensic procedures of existing investigation methodologies were taken into consideration when developing the fusion framework. The Marine Geo Navigator was implemented with the technologies of Machine Learning and Visualization to evaluate geolocations and paths of vessels. Through expert analysis, the proposed investigation framework was verified as a suitable process to be utilized in the Marine industry and the tool was validated resulting an acceptable level of accuracy.

关键词： Industries Wireless communication Root cause analysis Navigation Geology Digital forensics Transportation

来源：评论

学校读者我要写书评

暂无评论

Non-Negative Tensor Factorization of Infant Spontaneous Movements: A Pilot Study for ASD Risk Evaluation of Newborn Infants

Non-Negative Tensor Factorization of Infant Spontaneous Move...

引用

2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Yonei, Rikuya Furui, Akira Doi, Hirokazu Shimatani, Koji Hayashi, Hideaki Yamamoto, Midori Sakurai, Kenichi Mori, Chisato Tsuji, Toshio Hiroshima University Graduate School of Advanced Science and Engineering 1-4-1 Kagamiyama Hiroshima Higashi-Hiroshima739-8527 Japan Nagaoka University of Technology Department of Information and Management Systems Engineering 1603-1 Kamitomioka Niigata Nagaoka940-2188 Japan Prefectural University of Hiroshima Faculty of Health and Welfare 1-1 Gakuen-machi Hiroshima Mihara723-0053 Japan Osaka University Institute for Datability Science 2-8 Yamadaoka Osaka Suita565-0871 Japan Chiba University Center for Preventive Medical Sciences Department of Nutrition and Metabolic Medicine 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Chiba University Center for Preventive Medical Sciences Department of Sustainable Health Science 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Chiba University Graduate School of Medicine Department of Bioenvironmental Medicine 1-8-1 Inohana Chuo-ku Chiba260-8670 Japan

ISBN: (纸本)9798331531614

Early detection of infants with autism spectrum disorder (ASD) can lead to effective developmental support. In clinical practice, early screening of 18-month-old infants is implemented using a parent-completed questionnaire. However, research has suggested that signs of ASD may also appear in movement characteristics during the first few months of age. In this paper, we propose a method to evaluate infant movement from videos based on non-negative tensor factorization (NTF) and apply it to ASD risk assessment in the neonatal period. The proposed method applies NTF to pose data estimated from videos, and decomposes the infant's movements into multiple components while considering the linkage between body parts. In the experiment, we evaluated the effectiveness of the proposed method using 36 low-risk infants and 13 high-risk infants for ASD, with the aim of applying the method to ASD risk assessment. The results showed that the proposed method captured the tendency for infants to perform different movements depending on their risk level. Machine learning analysis revealed that the proposed method identified ASD risk with an accuracy exceeding 70%, which was comparable or superior to the existing video evaluation method based on heuristically designed indicators. © 2025 IEEE.

关键词： Non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses

引用

Genomics, Proteomics & Bioinformatics 2022年第5期20卷 912-927页

作者： Alexandra J.Lee Dallas L.Mould Jake Crawford Dongbo Hu Rani K.Powers Georgia Doing James C.Costello Deborah A.Hogan Casey S.Greene Genomics and Computational Biology Graduate Program University of PennsylvaniaPhiladelphiaPA 19104USA Department of Microbiology and Immunology Geisel School of Medicine at DartmouthHanoverNH 03755USA Department of Systems Pharmacology and Translational Therapeutics University of PennsylvaniaPhiladelphiaPA 19104USA Wyss Institute for Biologically Inspired Engineering Harvard UniversityBostonMA 02115USA Department of Pharmacology University of Colorado School of MedicineDenverCO 80045USA Center for Health AI University of Colorado School of MedicineDenverCO 80045USA Department of Biochemistry and Molecular Genetics University of Colorado School of MedicineDenverCO 80045USA

Genome-wide transcriptome profiling identifies genes that are prone to differential expression(DE)across contexts,as well as genes with changes specific to the experimental *** genes that are specifically changed in a context of interest from common differentially expressed genes(DEGs)allows more efficient prediction of which genes are specific to a given biological process under ***,common DEGs or pathways can only be identified through the laborious manual curation of experiments,an inordinately time-consuming *** we pioneer an approach,Specific cOntext Pattern Highlighting In Expression data(SOPHIE),for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be *** apply SOPHIE to diverse datasets including those from human,human cancer,and bacterial pathogen Pseudomonas *** identifies common DEGs in concordance with previously described,manually and systematically determined common *** molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional ***’s measure of specificity can complement log2 fold change values generated from traditional DE *** example,by filtering the set of DEGs,one can identify genes that are specifically relevant to the experimental condition of ***,these results can inform future research *** scripts used in these analyses are available at https://***/greenelab/*** can access https://***/greenelab/sophie to run SOPHIE on their own data.

关键词： Neural network Transcriptomics Software Differential expression analysis Machine learning

来源：评论

学校读者我要写书评

暂无评论

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS

arXiv

引用

arXiv 2022年

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. Coelho, Leandro dos S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data. © 2022, CC BY-NC-SA.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach

arXiv

引用

arXiv 2023年

作者： Sang, Linwei Xu, Yinliang Long, Huan Hu, Qinran Sun, Hongbin Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China The School of Electrical Engineering Southeast University Nanjing China The Department of Electrical Engineering State Key Laboratory of Power Systems Tsinghua University Beijing China

Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Implementation of an Efficient IoT Enabled Automated Paralysis Healthcare System

Implementation of an Efficient IoT Enabled Automated Paralys...

引用

IEEE International Conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Visalakshi Annepu Adnan Naji Jameel AL-Tamimi H.S. S. Aljibori Naga Raju Challa Kalapraveen Bagadi M. N. Mohammed Oday I. Abdullah M. Alfiras Rabab Alayham Abbas Helmi School of Comp. Science & Eng. VIT-AP University Amaravati India College of Technical Engineering AI-Farahidi University Baghdad Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Dept. of Elec. and Comm. Eng. Bapatla Engineering College Baptla India Mechanical Engineering Department College of Engineering Gulf University Sanad Bahrain Dept of Energy Engineering University of Baghdad Iraq Mechanical Engineering Dept College of Engineering Gulf University Sanad Bahrain Hamburg University of Technology Institute of Laser and Systems Technologies Hamburg Germany Electrical and Electronic Engineering Department College of Engineering Gulf University Sanad Kingdom of Bahrain School of Graduate Studies Management and Science University Shah Alam Selangor Malaysia

This system aims to provide comprehensive care to individuals with paralysis, including symptom monitoring, medical treatment, and rehabilitation. The IoT-based system comprises interconnected devices, such as sensors, wearables, and mobile apps that communicate with one another and with healthcare providers. The system can track patients' vital signs, activity levels, and medication adherence, and provide immediate feedback to healthcare providers. Additionally, the system can aid with rehabilitation exercises, send reminders, and gather data on progress to enhance treatment outcomes. The system's intelligent analytics can also assist healthcare providers in detecting patterns in patient behavior and tailoring treatment plans more effectively. Overall, an IoT-based paralysis healthcare system has the potential to transform healthcare delivery by offering personalized, real-time care and monitoring to individuals with paralysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A multi-objective model for sustainable reverse logistics design for end-of-life tires under a regulatory framework: A Chilean case study

A multi-objective model for sustainable reverse logistics de...

引用

Electrical, Computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Ignacio Castañeda Rodríguez Andrea Espinoza Pérez Hakim Boudaoud Department of Industrial Engineering University of Santiago of Chile Santiago Chile Department of Industrial Engineering Program for the Development of Sustainable Production Systems University of Santiago of Chile Santiago Chile Équipe de Recherche sur les Processus Innovatifs ERPI Université de Lorraine Nancy France

Growing concern about environmental pollution has led to a tightening of legislation worldwide. An example is the Extended Producer Responsibility (EPR) principle, which holds producers responsible for the life-cycle management of their products. In this context, the management of End-of-life tires (ELT) presents a significant challenge due to the pollution they cause by being disposed of in landfills without prior treatment. Thus, there is an opportunity to implement ELT recovery technologies that explore new sustainable alternatives. This work presents a multi-period, multi-product, multi-objective optimization model that supports decision-making for the design of a sustainable reverse logistics network. A case study is presented to show the usefulness of this model, considering the implementation of pyrolysis plants for the treatment of ELT in Chile under the regulatory framework of the EPR Law. The results show that even though the objectives are opposed, the regulatory framework of the EPR Law allows for mitigating the conflict between economic, environmental, and social decisions by establishing collection goals. The epsilon-constraint method can find economically profitable solutions, obtaining gaps of at least 8.5% and at most 19.3%. However, this will depend on the changes and sensitivity of the uncertain parameters of the model. Conclusions call for reinforcement of regulatory policies involving reverse logistics and ELT management. Future work considers the implementation of robust optimization to deal better with uncertainty.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：