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Journal of Economy and Technology 2025年 3卷 44-68页

作者： Irsan Hardi Ghalieb Mutig Idroes Yoshihiro Hamaguchi Muhlis Can Teuku Rizky Noviandy Rinaldi Idroes Department of Economics Faculty of Economics and Business Universitas Syiah Kuala Banda Aceh Indonesia Economic Modeling and Data Analytics Unit Graha Primera Saintifika Aceh Besar Indonesia Energy and Green Economics Unit Graha Primera Saintifika Aceh Besar Indonesia Department of Economics Faculty of Economics and Business Administration Kyoto University of Advanced Science Kyoto Japan Social Science Research Lab BETA Akademi Istanbul Turkey Interdisciplinary Innovation Research Unit Graha Primera Saintifika Aceh Besar 23371 Indonesia School of Mathematics and Applied Sciences Universitas Syiah Kuala Banda Aceh 23111 Indonesia

The growing awareness of the importance of transitioning to sustainable energy sources emphasizes the necessity of fostering business optimism toward renewable energy, as businesses wield significant influence in driving innovation and scaling up renewable energy deployment. Therefore, this study investigates the impact of business confidence on long-term renewable energy generation in selected major Asian economies: China, Japan, South Korea, Indonesia, and Turkey. Through country-specific assessments, we utilized three methods capable of yielding long-term empirical results: Fully-Modified OLS (FMOLS), Dynamic OLS (DOLS), and Canonical Cointegrating Regressions (CCR). The study also conducted a robustness check by utilizing the Robust Least Squares (RLS) method, preceded by multiple preliminary tests, to ensure the validity and reliability of the results. The findings show that businesses in all selected countries exhibit confidence toward long-term renewable energy. However, there are variations in the confidence level, with businesses in Japan, South Korea, and Turkey demonstrating high confidence while those in China and Indonesia show low confidence. The study also found a trade-off between business confidence levels and energy consumption. In Japan, South Korea, and Turkey, high business confidence correlates with a negative impact of energy consumption, while in Indonesia, low business confidence is aligned with a positive effect of energy consumption on renewable energy. This suggests that business confidence and energy consumption dynamics influence renewable energy development. Policy recommendations tailored to each of the selected countries are provided to address these findings, aiming to enhance business trust and optimism in renewable energy within major Asian economies.

关键词： Renewable Energy Business Confidence China Japan South Korea Indonesia Turkey

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Automated Pipeline for Regional Epicardial Adipose Tissue Distribution Analysis in the Left Atrium 15th

Automated Pipeline for Regional Epicardial Adipose Tissue Di...

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15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Lluch, Èric Castañeda, Eduardo Weiss, Maximilian E. R. Vizitiu, Anamaria Jacob, Athira Jami, Jitin Audigier, Chloé Meister, Felix Mihalef, Viorel Passerini, Tiziano Siemens Healthineers Digital Technologies and Innovation Erlangen Germany Pattern Recognition Lab Department of Computer Science Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Siemens SRL Brasov Romania Siemens Healthineers Digital Technology and Innovation Princeton United States Machine-Learning and Data Analytics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Advanced Clinical Imaging Technology Siemens Healthineers AG Lausanne Switzerland

ISBN: (纸本)9783031877551

Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting approximately 3% of the global population and rising to 11% in individuals over 80 years old. The distribution of epicardial adipose tissue (EAT) around the atria plays a significant role in the pathophysiology of AF, yet its regional analysis remains underexplored. This paper introduces an automated pipeline capable of segmenting 14 atrial regions from patient CT angiograms, a notable advancement over previous attempts to automate the creation of synthetic anatomical or physics-based twins. Our fully automatic pipeline requires no human supervision and provides the regional EAT quantification successfully on 91.9% of the analyzed patients for which the LA and its structures were automatically segmented (1797 out of 1954 patients), including the automatic segmentation of the aorta and pericardium to assess EAT volume around the left atrium. By applying principal component analysis of the regional EAT to analyze a separate dataset of 84 individuals who were clinically labeled, we identified a distinct group that contained all the patients with AF. This finding suggests that there is a significant link between the pattern of fat distribution and AF. This novel approach has the potential to enhance our understanding of AF and improve patient outcomes by enabling large-scale, regional fat analysis in an automated manner on all patients with accurate segmentation of left atrial structures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Principal component analysis

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MULABS: Multi-Task Learning with Attention-Based Scoring for Click-Through Rate Prediction on Sparse data in Healthcare Real-World Scenarios

MULABS: Multi-Task Learning with Attention-Based Scoring for...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Tu, Tu Zhang, Jing Wang, Yue Jin, Hui He, Megan Weihong IQVIA Inc. Data Science and Advanced Analytics Shanghai China

ISBN: (纸本)9781665468190

Personalized recommender systems have found wide applications in healthcare since it has the potential to inform individual users about the most needed medical resources. However, most recommender systems levering deep learning techniques demand a rich amount of data to learn patterns. In this paper, we aim to improve the performance of a recommender system when data are sparse and in low volume. In particular, we introduce a multi-task learning approach to alleviate the learning pressure and propose an attention-based scheme to boost the model performance by fully utilizing users' historical data. In the experiments, we develop a novel dataset from a public dataset to mimic the real-world scenarios in healthcare. The results demonstrate that this multi-task learning with attention-based scoring (MULABS) aided recommender system is more accurate in predicting user preference with an AUC of 0.7541, requiring limited computation and memory resources. © 2022 IEEE.

关键词： Recommender systems

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Contamination, biofilm formation, virulence and molecular characterization of multi–drug resistant Staphylococcus aureus isolated from pork under low temperature in Zhejiang Province, China

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SSRN 2025年

作者： Zhai, Ruidong Liang, Yilun Lei, Lei Xu, Xinlu Wu, Qin Han, Yue Deng, Simin Xu, Jiali Xia, Jing Yang, Yongchun Cocolin, Luca Simone Rantsiou, Kalliopi Cheng, Changyong Song, Houhui Key Laboratory of Applied Biotechnology on Animal Science & Veterinary Medicine of Zhejiang Province Zhejiang Engineering Research Center for Veterinary Diagnostics & Advanced Technology Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management Belt and Road International Joint Laboratory for One Health and Food Safety China-Australia Joint Laboratory for Animal Health Big Data Analytics College of Veterinary Medicine of Zhejiang Agricultural and Forestry University Zhejiang Province Hangzhou China Department of Agricultural Forest and Food Sciences University of Turin Torino Italy

Staphylococcus aureus is considered to be an increasingly food safety concern. Low temperature is usually used to limit bacterial reproduction on meat. The aim of this study was to investigate the contamination and characterization about S. aureus on pork under low temperature at cold storage warehouses in Zhejiang Province, China. A total of 344 samples were collected to detect S. aureus, and All 52 S. aureus were isolated from 11.6% samples with present in 18 batches from 13 cold storage warehouses, of note, 51.9% isolates was enterotoxigenic, and 71.2% isolates were multi-drug resistant strains with strong biofilm formation, notably, 13 MRSA. Moreover, all 52 isolates were classified into 10 clusters of RAPD with 13 SPA types, especially the new frequently Spa-t20663. And ST398, ST188, ST6 types were ranked among the top three accompanied by close genetic relationships with other food-borne S. aureus in China. In addition, high virulence isolates were observed on Galleria mellonella, and ST188 S. aureus with skin abscesses in rabbits. Findings from this study highlight the contamination, long-term survival, virulence and diverse molecular characteristics of multi-drug resistant S. aureus isolated from frozen pork. Therefore, integrated surveillance and control measures are urgent to mitigate the associated risk. © 2025, The Authors. All rights reserved.

关键词： Meats

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Randomization-Based Inference for MCP-Mod

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Statistics in Medicine 2025年第10-12期44卷 e70092页

作者： Lukas Pin Oleksandr Sverdlov Frank Bretz Björn Bornkamp MRC Biostatistics Unit University of Cambridge Cambridge UK Early Development Analytics Novartis Pharmaceuticals Corporation East Hanover NJ USA Advanced Methodology and Data Science Novartis Pharma AG Basel Switzerland Center for Medical Data Science Institute for Medical Statistics Medical University of Vienna Vienna Austria

Dose selection is critical in pharmaceutical drug development, as it directly impacts therapeutic efficacy and patient's safety of a drug. The Generalized Multiple Comparison Procedures and Modeling approach is commonly used in Phase II trials for testing and estimation of dose-response relationships. However, its effectiveness in small sample sizes, particularly with binary endpoints, is hindered by issues like complete separation in logistic regression, leading to non existence of estimates. Motivated by an actual clinical trial using the MCP-Mod approach, this paper introduces penalized maximum likelihood estimation (MLE) and randomization-based inference techniques to address these challenges. Randomization-based inference allows for exact finite sample inference, while population-based inference for MCP-Mod typically relies on asymptotic approximations. Simulation studies demonstrate that randomization-based tests can enhance statistical power in small to medium-sized samples while maintaining control over type-I error rates, even in the presence of time trends. Our results show that residual-based randomization tests using penalized MLEs not only improve computational efficiency but also outperform standard randomization-based methods, making them an adequate choice for dose-finding analyses within the MCP-Mod framework. Additionally, we apply these methods to pharmacometric settings, demonstrating their effectiveness in such scenarios. The results in this paper underscore the potential of randomization-based inference for the analysis of dose-finding trials, particularly in small sample contexts.

关键词： dose finding finite sample inference multiple testing penalized maximum likelihood estimation randomization test time trends

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A Workflow for Causal Discovery Under Evolving Mechanisms

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SSRN 2025年

作者： Rocchi, Niccolò Zanga, Alessio Bernasconi, Alice Gronchi, Alessandro Callegaro, Dario Borghi, Alessandra Casali, Paolo Giovanni Provenzano, Salvatore Miceli, Rosalba Trama, Annalisa Stella, Fabio University of Milano-Bicocca Viale Sarca 336 Milan20126 Italy Department of Epidemiology and Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Via Giacomo Venezian 1 Milan20133 Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd Grenzacherstrasse 124 Basel4070 Switzerland Department of Surgery Fondazione IRCCS Istituto Nazionale dei Tumori Via Giacomo Venezian 1 Milan20133 Italy Department of Cancer Medicine Fondazione IRCCS Istituto Nazionale dei Tumori Via Giacomo Venezian 1 Milan20133 Italy Biostatistics for Clinical Research Unit Fondazione IRCCS Istituto Nazionale dei Tumori Via Giacomo Venezian 1 Milan20133 Italy

Objective: Causal networks are gaining significant attention in healthcare. They sup-port physicians by offering a mechanistic view of the phenomena under investigation, thusenabling personalized, explainable, and optimal decisions. However, developing causalnetworks is complex, and the problem becomes even more evident when dealing withrare diseases. Observational data and events are scarce here, medical knowledge aloneis often insufficient to obtain an effective model, and disease mechanisms may evolveover time. In this paper, we design, develop, and evaluate a collaborative procedure forlearning causal relationships in constrained-resources ***: We devise a workflow that iteratively learns and refines causal networks’structures over different time windows. The workflow leverages a novel pre-processingalgorithm for survival data, characterized by longitudinal data measuring specific events’occurrence and timing. Our methodology takes advantage of the continuous interactionwith domain experts, who can provide insights into the temporal evolution of causalmechanisms. We evaluate the proposed framework on soft tissue sarcoma data, a classof rare ***: We obtain three different networks, related to three overlapping time *** our knowledge, this is the first cause-effect representation of the natural history ofsoft tissue sarcoma. We assessed the results with clinical experts, collecting key insightsinto the disease’s ***: The proposed methodology paves the way to standardized, replicable, andagile causal discovery in all situations affected by sparse data, longitudinal dynamics,and heterogeneous expert knowledge. Results on soft tissue sarcoma may enable moreeffective and personalized clinical decisions. © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Detection of Breast Cancer using Quasi-Cross Focused Transformer-Guided Quantum Network with Lotus Optimization

Detection of Breast Cancer using Quasi-Cross Focused Transfo...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Rajesh Munirathnam Vishal Shukla Deepashree Devaraj Chintan Thacker Ujwal Nandekar Natrayan L Data & Analytics Division TechnoGen Inc. Hillsborough New Jersey USA Department of Mechanical Engineering Ramdeobaba College of Engineering and Management Nagpur Maharashtra Electronics and Instrumentation Engineering RV College of Engineering Bengaluru Karnataka Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Post Limda Gujarat Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Pune Symbiosis International (Deemed University) Pune Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai TamilNadu

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Patient outcomes improve if breast cancer is discovered early. Although many deep learning methods have been studied for breast cancer identification, the efficacy of many has been limited. It presents a new method encompassing the Quasi Cross Focused Transformer Guided Quantum Network with Lotus Optimization (QCFT-GQNet-LO) which is novel to resolve these issues. This technique uses Quasi-Cross Filtering (QCF) for image pre-processing, which improves image quality by reducing noise and artefacts. It was evaluated on histopathological images from the MIAS (Mammographic Image Analysis Society) dataset. A Focused Transformer with U-Net (FTUNet) is utilized to precisely segment malignant regions, guaranteeing precise identification of crucial places. The Self-Guided Quantum Network with Triple Attention (SGQNet-TA) controls classification and feature extraction, significantly improving the model's diagnostic abilities. The whole system is optimized using the Lotus Effect Optimization Algorithm (LEOA) to improve the efficiency and robustness. The QCFT-GQNetLO approach is shown to outperform other approaches, with experimentally demonstrated accuracy of 99.9%, and recall rate of 99.8% on the MIAS dataset. This significantly improves diagnostic precision of lesion type categorization and early breast malignancy diagnosis in histopathological images.

关键词： Image segmentation Accuracy Filtering Noise Transformers Breast cancer Robustness Quantum networks Lesions Optimization

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A Survey and Experimental Review on data Distribution Strategies for Parallel Spatial Clustering Algorithms

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Journal of Computer science & Technology 2024年第3期39卷 610-636页

作者： Jagat Sesh Challa Navneet Goyal Amogh Sharma Nikhil Sreekumar Sundar Balasubramaniam Poonam Goyal Advanced Data Analytics and Parallel Technologies Laboratory Birla Institute of Technology and Science Pilani 333031India Uber New York 11101U.S.A. Computer Science and Engineering Department University of MinnesotaMinneapolis 55455U.S.A.

The advent of Big data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks such as MPI,MapReduce,and *** important step for any parallel clustering algorithm is the distribution of data amongst the cluster *** step governs the methodology and performance of the entire *** typically use random,or a spatial/geometric distribution strategy like kd-tree based partitioning and grid-based partitioning,as per the requirements of the ***,these strategies are generic and are not tailor-made for any specific parallel clustering *** this paper,we give a very comprehensive literature survey of MPI-based parallel clustering algorithms with special reference to the specific data distribution strategies they *** also propose three new data distribution strategies namely Parameterized Dimensional Split for parallel density-based clustering algorithms like DBSCAN and OPTICS,Cell-Based Dimensional Split for dGridSLINK,which is a grid-based hierarchical clustering algorithm that exhibits efficiency for disjoint spatial distribution,and Projection-Based Split,which is a generic distribution *** of these preserve spatial locality,achieve disjoint partitioning,and ensure good data load *** experimental analysis shows the benefits of using the proposed data distribution strategies for algorithms they are designed for,based on which we give appropriate recommendations for their usage.

关键词： parallel data mining data distribution parallel clustering spatial locality preservation

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Mechanistic Insights into Cadmium-Induced Nephrotoxicity: Nrf2-Driven Ho-1 Activation Promotes Ferroptosis Via Iron Overload and Oxidative Stress

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SSRN 2025年

作者： Luo, Tongwang Song, Shengzhe Wang, Shujie Zhou, Bin Song, Quanjiang Jiang, Sheng Shen, Lingjun Wang, Xiaodu Song, Houhui Shao, Chunyan College of Veterinary Medicine Zhejiang A&F University Zhejiang Hangzhou311300 China Key Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Hangzhou311300 China Zhejiang Engineering Research Center for Veterinary Diagnostics & Advanced Technology Zhejiang Hangzhou311300 China Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management Zhejiang Hangzhou311300 China Belt and Road International Joint Laboratory for One Health and Food Safety Zhejiang Hangzhou311300 China China-Australia Joint Laboratory for Animal Health Big Data Analytics Zhejiang Hangzhou311300 China College of Animal Science and Technology College of Animal Science and Technology College of Veterinary Medicine China

Cadmium (Cd), a pervasive environmental toxicant, poses significant threats to human and animal health through multi-organ toxicity. While ferroptosis has been implicated in Cd-induced pathologies, the molecular mechanisms underlying Cd-mediated nephrotoxicity remain poorly understood. This study elucidates the ferroptosis pathway in CdCl2-exposed PK-15 cells and murine kidney, characterized by iron overload, lipid peroxidation, and mitochondrial dysfunction, which were ameliorated by ferroptosis inhibitor ferrostatin-1. Transcriptomic analysis revealed substantial upregulation of heme oxygenase-1 (HO-1) following CdCl2 exposure. Mechanistically, CdCl2 triggered nuclear translocation of nuclear factor erythroid 2-related factor-2 (NRF2), subsequently activating Ho-1 transcription. Over-activated HO-1 catalyzes the decomposition of heme and releases free iron, accompanied with the degradation of ferritin heavy chain 1 (FTH1) induced by CdCl2 exposure, which leads to intracellular iron overload and excessive lipid peroxides production through Fenton reaction, resulting in ferroptosis ultimately. In vivo validation confirmed NRF2/HO-1-mediated ferroptosis in CdCl2-induced murine nephrotoxicity. Both pre-treatment with HO-1 competitive inhibitor Zinc protoporphyrin IX (ZnPP) and knockout of Ho-1 gene remarkably alleviated PK-15 cells against ferroptosis induced by CdCl2 treatment. Our findings demonstrate that Cd exposure initiates NRF2-mediated HO-1 overexpression, driving iron-dependent lipid peroxidation and subsequent ferroptosis. This mechanistic insight provides potential therapeutic targets for mitigating Cd-induced renal damage, advancing our understanding of heavy metal toxicity and its cellular consequences. © 2025, The Authors. All rights reserved.

关键词： Transcription

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Parallelizing OPTICS for multicore systems 14

Parallelizing OPTICS for multicore systems

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7th ACM India Computing Conference, COMPUTE 2014

作者： Goyal, Poonam Kumari, Sonal Kumar, Dhruv Balasubramaniam, Sundar Goyal, Navneet Advanced Data Analytics and Parallel Technologies Laboratory Department of Computer Science and Information Systems BITS-Pilani Pilani India

ISBN: (纸本)9781605588148

Parallelizing algorithms to leverage multiple cores in a processor or multiple nodes in a cluster setup is the only way forward to handle ever-increasing volumes of data. OPTICS is a well-known density based clustering algorithm to identify arbitrary shaped clusters. Since, hierarchical cluster ordering of OPTICS is sensitive to the order in which data is processed, typically a priority queue is used to maintain the order. This sequential access order makes it difficult to parallelize OPTICS. Moreover, the execution time of OPTICS increases with increase in density of data. We propose a parallel version of OPTICS for shared memory multi-core systems using a master-slave pattern for parallelization. The master runs concurrently with the slaves and distributes data to the slaves. Each slave performs neighborhood queries for a subset of data. Our approach ensures that cluster ordering matches with that of the classical OPTICS. Our solution runs in a mostly data parallel mode yielding scalable performance. We also argue that our approach is well suited for dense datasets in particular. Copyright 2014 ACM.

关键词： Hierarchical systems

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