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Facile Synthesis of Flower-Like Bismuth Oxybromide Anchored on ZSM-5 for Enhanced Photocatalytic Dye Degradation

South African Journal of Chemical Engineering 2025年

作者： Wahyu Prasetyo Utomo Nor Farida Binta Aisyah Ahmad Azzam Babgei Nur Karimah Ade Irma Rozafia Imam Susilo Tri Widjaja Hadi Nur Retno Sari Rui Liu Djoko Hartanto Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember (ITS) Sukolilo Surabaya 60111 East Java Indonesia Faculty of Medicine and Health Institut Teknologi Sepuluh Nopember (ITS) Sukolilo Surabaya 60111 East Java Indonesia Department of Chemical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember (ITS) Sukolilo Surabaya 60111 East Java Indonesia Department of Chemistry Faculty of Mathematics and Natural Science Universitas Negeri Malang Indonesia Department of Pharmaceutics Faculty of Pharmacy Airlangga University Surabaya Indonesia School of Energy and Environment City University of Hong Kong Kowloon 999077 Hong Kong SAR

The morphology of the photocatalyst plays an important role in determining its photocatalytic activity. Bismuth oxybromide (BiOBr) offers the advantages of a unique layered structure, easy preparation, and narrow band gaps. However, a fine morphology of flower-like BiOBr is generally prepared using a relatively complex method, requiring high energy consumption. In this work, we report a facile method to prepare fine flower-like BiOBr, employing a coprecipitation method at room temperature with the addition of polyvinyl pyrrolidone as a capping agent. Further treatment by adding ZSM-5 during the synthesis process leads to the growth of flower-like BiOBr on the surface of ZSM-5, forming a BiOBr/ZSM composite. Photocatalytic test over flower-like BiOBr and flower-like BiOBr/ZSM-5 showed an appreciable performance, reaching the degradation efficiency of methylene blue up to 50.96% and 87.29%, respectively upon 120 min of UV illumination, which is higher than the bare irregular BiOBr (31.82%). The enhanced photodegradation performance can be attributed to the more efficient charge transfer and enlarged surface area, induced by the thin flower-like morphology of BiOBr and the presence of ZSM-5. This work provides insight into facile synthesis and surface modification of BiOBr to enhance its catalytic activity.

关键词： Bismuth oxyhalide zeolite photocatalysis waste water

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Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S

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Biodata Mining 2023年第1期16卷 35-35页

作者： Yang, Tzong-Hann Chen, Yu-Fu Cheng, Yen-Fu Huang, Jue-Ni Wu, Chuan-Song Chu, Yuan-Chia Department of Otorhinolaryngology Taipei City Hospital Taipei 100 Taiwan General Education Center University of Taipei Taipei 10671 Taiwan Department of Speech-Language Pathology and Audiology National Taipei University of Nursing and Health Sciences Taipei 112303 Taiwan Department of Otolaryngology-Head and Neck Surgery School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Medical Research Taipei Veterans General Hospital Taipei 112 Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei 112 Taiwan Department of Otolaryngology-Head and Neck Surgery Taipei Veterans General Hospital Taipei 112 Taiwan Institute of Brain Science National Yang Ming Chiao Tung University Taipei 112 Taiwan College of Science and Engineering Fu Jen University Taipei 243 Taiwan Information Management Office Taipei Veterans General Hospital Taipei 112 Taiwan Big Data Center Taipei Veterans General Hospital Taipei 112 Taiwan Department of Information Management National Taipei University of Nursing and Health Sciences Taipei 112 Taiwan

Objectives: The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods: We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results: On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions: Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely. © 2023, The Author(s).

关键词： Age-related Hearing loss HHIE-S Innovation LGBM Machine learning Predictive enhancement

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The phase diagram of a deep potential water model

arXiv

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arXiv 2021年

作者： Zhang, Linfeng Wang, Han Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China

Using the Deep Potential methodology, we construct a model that reproduces accurately the potential energy surface of the SCAN approximation of density functional theory for water, from low temperature and pressure to about 2400 K and 50 GPa, excluding the vapor stability region. The computational efficiency of the model makes it possible to predict its phase diagram using molecular dynamics. Satisfactory overall agreement with experimental results is obtained. The fluid phases, molecular and ionic, and all the stable ice polymorphs, ordered and disordered, are predicted correctly, with the exception of ice III and XV that are stable in experiments, but metastable in the model. The evolution of the atomic dynamics upon heating, as ice VII transforms first into ice VII′′ and then into an ionic fluid, reveals that molecular dissociation and breaking of the ice rules coexist with strong covalent fluctuations, explaining why only partial ionization was inferred in experiments. © 2021, CC BY-NC-ND.

关键词： Phase diagrams

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ADVERSARIAL ROBUSTNESS OF DEEP CONVOLUTIONAL CANDLESTICK LEARNER

arXiv

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arXiv 2020年

作者： Chen, Jun-Hao Chen, Samuel Yen-Chi Tsai, Yun-Cheng Shur, Chih-Shiang Department of Computer Science & Information Engineering National Taiwan University Taipei10617 Taiwan Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States School of Big Data Management Soochow University Taipei11102 Taiwan Department of Business Administration National Sun Yat-sen University Taipei10617 Taiwan

Deep learning (DL) has been applied extensively in a wide range of fields. However, it has been shown that DL models are susceptible to a certain kinds of perturbations called adversarial attacks. To fully unlock the power of DL in critical fields such as financial trading, it is necessary to address such issues. In this paper, we present a method of constructing perturbed examples and use these examples to boost the robustness of the model. Our algorithm increases the stability of DL models for candlestick classification with respect to perturbations in the input data. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Coherent photoproduction of low-pT charmonium in peripheral heavy ion collisions within the color dipole model

arXiv

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arXiv 2021年

作者： Yu, Gongming Cai, Yanbing Fu, Yongping Yang, Haitao Gao, Quangui Hu, Qiang Hu, Liyuan Li, Wei Song, Yushou Fundamental Science on Nuclear Safety and Simulation Technology Laboratory Harbin Engineering University Harbin150000 China Guizhou Key Laboratory in Physics and Related Areas Guizhou Key Laboratory of Big Data Statistic Analysis Guizhou University of Finance and Economics Guiyang550025 China Department of Physics West Yunnan University Lincang677000 China Department of Physics Zhaotong University Zhaotong657000 China Department of Physics Yuxi Normal University Yuxi653100 China Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China

We calculate the centrality dependence for coherent photoproduction of very low-pT J/ψ at Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) energies within the impact parameter dependent saturated color dipole model. By using the large equivalent photon fluxes, we present the differential cross section of very low-pT J/ψ produced by coherent photonuclear in peripheral heavy-ion collisions. The numerical results demonstrate that our calculation are agree with J/ψ data in peripheral heavy ion collisions at Relativistic Heavy Ion Collider (RHIC) energies. © 2021, CC BY.

关键词： Colliding beam accelerators

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Seeing biodiversity: Perspectives in machine learning for wildlife conservation

arXiv

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arXiv 2021年

作者： Tuia, Devis Kellenberger, Benjamin Beery, Sara Costelloe, Blair R. Zuffi, Silvia Risse, Benjamin Mathis, Alexander Mathis, Mackenzie Weygandt van Langevelde, Frank Burghardt, Tilo Kays, Roland Klinck, Holger Wikelski, Martin Couzin, Iain D. van Horn, Grant Crofoot, Margaret C. Stewart, Charles V. Berger-Wolf, Tanya Switzerland United States Max Planck Institute of Animal Behavior Germany Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany Department of Biology University of Konstanz Germany Institute for Applied Mathematics and Information Technologies IMATI-CNR Italy Computer Science Department University of Münster Germany Switzerland Environmental Sciences Group Wageningen University Netherlands Computer Science Department University of Bristol United Kingdom Department of Forestry and Environmental Resources North Carolina State University United States North Carolina Museum of Natural Sciences United States Cornell Lab of Ornithology Cornell University United States Department of Computer Science Rensselaer Polytechnic Institute United States Translational Data Analytics Institute Ohio State University United States

data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation. © 2021, CC BY-NC-SA.

关键词： Ecology

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P18.04.B SINGLE CELL TRANSCRIPTOMIC ANALYSIS IDENTIFIES DISTINCT INTRATUMORAL SUBCLONES IN MENINGIOMA

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii122–ii122页

作者： Blume, C Tang, Y Dogan, H Ritter, M Dentro, S von Deimling, A Gerstung, M Schlesner, M Sahm, F Maas, S L N University Hospital Heidelberg Dept. of Neuropathology Heidelberg Germany German Cancer Research Center (DKFZ) German Consortium for Translational Cancer Research (DKTK) Clinical Cooperation Unit Neuropathology Heidelberg Germany Division of AI in Oncology German Cancer Research Center (DKFZ) Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton United Kingdom Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany Department of Pathology Leiden University Medical Center Leiden Netherlands Department of Pathology Erasmus Medical Center Rotterdam Netherlands

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WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

arXiv

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arXiv 2021年

作者： Luo, Xiangde Liao, Wenjun Xiao, Jianghong Chen, Jieneng Song, Tao Zhang, Xiaofan Li, Kang Metaxas, Dimitris N. Wang, Guotai Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital and Institute Sichuan Cancer Center Chengdu China Department of Radiation Oncology Cancer Center West China Hospital Sichuan University Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China School of Medicine University of Electronic Science and Technology of China Chengdu China Department of Computer Science Johns Hopkins University Baltimore United States SenseTime Research Shanghai China West China Hospital-SenseTime Joint Lab West China Biomedical Big Data Center Sichuan University Chengdu China Department of Computer Science Rutgers University PiscatawayNJ United States

Whole abdominal organ segmentation is important in diagnosing abdomen lesions, radiotherapy, and follow-up. However, oncologists’ delineating all abdominal organs from 3D volumes is time-consuming and very expensive. Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation. In this work, we establish a new large-scale Whole abdominal ORgan dataset (WORD) for algorithm research and clinical application development. This dataset contains 150 abdominal CT volumes (30495 slices). Each volume has 16 organs with fine pixel-level annotations and scribble-based sparse annotations, which may be the largest dataset with whole abdominal organ annotation. Several state-of-the-art segmentation methods are evaluated on this dataset. And we also invited three experienced oncologists to revise the model predictions to measure the gap between the deep learning method and oncologists. Afterwards, we investigate the inference-efficient learning on the WORD, as the high-resolution image requires large GPU memory and a long inference time in the test stage. We further evaluate the scribble-based annotation-efficient learning on this dataset, as the pixel-wise manual annotation is time-consuming and expensive. The work provided a new benchmark for the abdominal multi-organ segmentation task, and these experiments can serve as the baseline for future research and clinical application development. © 2021, CC BY.

关键词： Large dataset

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Transcriptional and Neurochemical Signatures of Cerebral Blood Flow Alterations in Individuals With Schizophrenia or at Clinical High Risk for Psychosis

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Biological Psychiatry 2025年

作者： Knight, Samuel R. Abbasova, Leyla Zeighami, Yashar Hansen, Justine Y. Martins, Daniel Zelaya, Fernando Dipasquale, Ottavia Liu, Thomas Shin, David Bossong, Matthijs Azis, Matilda Antoniades, Mathilde Howes, Oliver D. Bonoldi, Ilaria Egerton, Alice Allen, Paul McGuire, Philip Modinos, Gemma Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Centre for Developmental Neurobiology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Medical Research Council Centre for Neurodevelopmental Disorders King's College London London United Kingdom Douglas Research Centre Department of Psychiatry McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Olea Medical La Ciotat France Centre for Functional MRI University of California San Diego San Diego CA United States Global MR Applications and Workflow GE Healthcare Menlo Park CA United States Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Department of Psychiatry Brain Center Rudoph Magnus University Medical Center Utrecht Utrecht Netherlands Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA United States Department of Psychiatry Oxford University Oxford United Kingdom

Background: The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behavior. Understanding relationships across these scales may be fundamental to advancing understanding of brain function in health and disease. Recent neuroimaging research has shown that functional brain alterations that are associated with schizophrenia spectrum disorders (SSDs) are already present in young adults at clinical high risk for psychosis (CHR-P), but the cellular and molecular determinants of these alterations remain unclear. Methods: Here, we used regional cerebral blood flow (rCBF) data from 425 individuals (122 with an SSD compared with 116 healthy control participants [HCs] and 129 individuals at CHR-P compared with 58 HCs) and applied a novel pipeline to integrate brainwide rCBF case-control maps with publicly available transcriptomic data (17,205 gene maps) and neurotransmitter atlases (19 maps) from 1074 healthy volunteers. Results: We identified significant correlations between astrocyte, oligodendrocyte, oligodendrocyte precursor cell, and vascular leptomeningeal cell gene modules for both SSD and CHR-P rCBF phenotypes. Additionally, endothelial cell genes were correlated in SSD, and microglia in CHR-P. Receptor distribution significantly predicted case-control rCBF differences, with dominance analysis highlighting dopamine (D₁, D₂, dopamine transporter), acetylcholine (VAChT, M₁), gamma-aminobutyric acid A (GABA_A), and glutamate (NMDA) receptors as key predictors for SSD (R²_adjusted = 0.58, false discovery rate [FDR]–corrected p <.05) and CHR-P (R²_adjusted = 0.6, p_FDR <.05) rCBF phenotypes. These associations were primarily localized in subcortical regions and implicate cell types involved in stress response and inflammation, alongside specific neuroreceptor systems, in shared and distinct rCBF phenotypes in psychos

关键词： Arterial spin labeling Biomarkers Gene expression Mental health Neuroimaging Neurotransmitters

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Determination factors of internet advertising affecting internet users’ behavior using canonical correlation analysis

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AIP Conference Proceedings 2024年第1期2895卷

作者： Farah Nabilah Zabidi Nurain Ibrahim Nur Izzah Atiqah Nurul Athirah Norzaidi Salsabilla Kamaludin Adina Najwa Kamarudin School of Mathematical Sciences College of Computing Informatics and Media Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Regional Department Suu Balm Malaysia Livemed Sdn Bhd Aurora Place Plaza No 1 Persiaran Bukit Jalil 57000 Kuala Lumpur Malaysia Federal Government Administrative Centre Block C6 Complex C Lebuh Perdana Selatan Presint 1 62514 Putrajaya Wilayah Persekutuan Putrajaya Malaysia Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

One of the most important and fundamental parts of today’s business world is advertising. Advertising is necessary for both business and consumers. We can now observe that the advertising industry has experienced substantial changes and is now employed globally. An advertising company has found this platform to be quite useful for increasing revenue by influencing the online users’ behaviour, which may change in either a positive or bad way. Therefore, the primary topic of this study paper is on how internet advertising affects consumers’ online behaviour in UiTM Shah Alam. Through surveys, we gathered information about two sets of variables: Internet Users’ Behaviour (Purchase Intention, Effective Medium, Frequency of Browsing, and Demographic Variable), and sets of variables Internet Advertising (Webpage Content, Informativeness and Interactivity). To determine the aspects of internet advertising influencing internet users’ behaviour, Canonical Correlation Analysis was applied to the data that was gathered. Internet advertising and internet users’ behaviour are significantly correlated, according to the results analysis. As a result, Internet advertising plays a big role in influencing consumers’ behaviour because of its close connection to their purchasing decisions. Based on findings, it can be concluded that internet advertising plays significant roles in examining internet users’ behaviour, which may be advantageous for many industries globally, particularly in current era of digitalization. With information on consumer behaviour both now and in the future, these findings can assist marketers and advertisers in developing more effective online advertising strategies that better engage young adults’ attention and influence their behaviour, particularly with regard to purchase intention, leading to a misalignment. It will then be able to create more successful online advertising.

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