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arXiv 2023年

作者： Stratton, Benjamin Hsieh, Chung-Yun Skrzypczyk, Paul Quantum Engineering Centre for Doctoral Training H. H. Wills Physics Laboratory Department of Electrical & Electronic Engineering University of Bristol BS8 1FD United Kingdom H.H. Wills Physics Laboratory University of Bristol Tyndall Avenue BristolBS8 1TL United Kingdom CIFAR Azrieli Global Scholars Program CIFAR Toronto Canada

Information is instrumental in our understanding of thermodynamics. Their interplay has been studied through completely degenerate Hamiltonians whereby the informational contributions to thermodynamic transformations can be isolated. In this setting, all states other then the maximally mixed state are considered to be in informational non-equilibrium. An important yet still open question is: how to characterise the ability of quantum dynamics to maintain informational non-equilibrium? Here, the dynamical resource theory of informational non-equilibrium preservability is introduced to begin providing an answer to this question. A characterisation of the allowed operations is given for qubit channels and the n dimensional Weyl-covariant channels - a physically relevant subset of the general channels. An operational interpretation of a state discrimination game with Bell state measurements is given. Finally, an explicit link between a channels classical capacity and its ability to maintain informational non-equilibrium is made. Copyright © 2023, The Authors. All rights reserved.

关键词： Thermodynamics

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Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network

arXiv

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arXiv 2024年

作者： Panek, Dominik Rząca, Carina Szczypior, Maksymilian Sorysz, Joanna Misztal, Krzysztof Baster, Zbigniew Rajfur, Zenon Doctoral School of Exact and Natural Sciences Jagiellonian University ul. Lojasiewicza 11 Kraków30-348 Poland Department of Molecular and Interfacial Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate Program in Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate program in Biomedical Engineering Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 Building B-1 Kraków30-059 Poland Division of Computational Mathematics Faculty of Mathematics and Computer Science Jagiellonian University 6 Lojasiewicza St. Kraków Poland

High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial network (GAN) for contrast transfer between two different separate microscopy systems: a confocal microscope (producing HQ images) and a wide-field fluorescence microscope (producing LQ images). Our model proves that such transfer is possible, allowing us to receive HQ-generated images characterized by low mean squared error (MSE) values, high structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR) values. For our best model in the case of comparing HQ-generated images and HQ-ground truth images, the median values of the metrics are 6·10-4, 0.9413, and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of comparison between LQ and HQ ground truth median values of the metrics are equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively. Therefore, we observe a significant increase ranging from 14% to 49% for SSIM and PSNR respectively. These results, together with other single-system cross-modality studies, provide proof of concept for further implementation of a cross-system biological image quality enhancement. © 2024, CC BY-SA.

关键词： Fluorescence imaging

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Assessment and optimization of As(V) adsorption on hydrogel composite integrating chitosan-polyvinyl alcohol and Fe3O4 nanoparticles and evaluation of their regeneration and reusable capabilities in aqueous media

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Science of the Total Environment 2023年 855卷 158877页

作者： Weerasundara, Lakshika Ok, Yong Sik Kumarathilaka, Prasanna Marchuk, Alla Bundschuh, Jochen School of Civil Engineering and Surveying Faculty of Health Engineering and Sciences University of Southern Queensland West Street ToowoombaQLD4350 Australia Korea Biochar Research Centre & Division of Environmental Sciences and Ecological Engineering Korea University Seoul Korea Republic of Institute for Life Sciences and the Environment University of Southern Queensland West Street ToowoombaQLD4350 Australia Doctoral Program in Science Technology Environment and Mathematics Department of Earth and Environmental Sciences National Chung Cheng University 168 University Rd. Min-Hsiung Chiayi County62102 Taiwan

A modified chitosan-polyvinyl alcohol (PVA) hydrogel was developed by incorporating Fe3O4 nanoparticles. Four chitosan-Fe3O4 (ChFe) hydrogel types were developed based on chitosan:Fe3O4 ratio as 1:0, 1:1, 1:0.5 and 1:0.25. Batch sorption experiments were conducted with different pH, dosage, kinetics, and isotherms. The exhausted ChFe hydrogels were evaluated for their regeneration and reuse capability with different acids and bases. The best hydrogel for arsenic (V) [As(V)] adsorption was 1:0.5 ratio ChFe hydrogel. The highest As(V) adsorption (89 %) was at pH 4 and the adsorption capacity gradually decreased with increasing solution pH. Within the pH 4–6 range, the hydrogel surface became positively charged due to protonation of –NH2 and –OH groups in the polymer chain and the positive surface attracted H2AsO4− and HAsO42− oxyanions. The experimental kinetic data was well-fitted to the Elovich model (R2 of 0.99) while the Freundlich isotherm model best described the isotherm data (R2 of 0.97). The models predicted chemisorption mechanisms on ChFe hydrogel composites. Electrostatic attractions with –NH3+ and –OH2+, ligand-exchange inner-sphere complexes formation and bidentate corner-sharing (2C) and bidentate edge-sharing (2E) trimetric surface complexes formation have been proposed as the adsorption mechanism of As(V) into ChFe hydrogel. 0.1 M CH3COOH showed the best regeneration pattern with 75, 96, 81, 53 and 43 % of 1st, 2nd, 3rd, 4th and 5th adsorption respectively. Because of this re-usable capability, the As(V) adsorption capacity is not a single value from one adsorption cycle, but a cumulative value of several adsorption cycles and it was 17.39 mg/g for five adsorption cycles. Open for regeneration and reuse, no post-treatment is needed for adsorbent-water separation, allow applications of the ChFe hydrogel composite in a wide range of applications such as water filtration and purification systems. The modification with ChFe further expands the application

关键词： Nanoparticles

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Specific Chemical Bioexergy Analysis and Microwave-Assisted Torrefaction Optimization Via Statistical and Artificial Intelligence Approaches

SSRN

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SSRN 2022年

作者： Chen, Wei-Hsin Aniza, Ria Department of Aeronautics and Astronautics National Cheng Kung University Tainan701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung411 Taiwan International Doctoral Degree Program on Energy Engineering National Cheng Kung University Tainan701 Taiwan

The artificial neural network (ANN) in artificial intelligence (AI) is a computational model that portrays how nerve cells (neurons) work in the human brain. Meanwhile, specific chemical bioexergy (SCB) is a vital indicator to provide essential information on how high the actual energy may be contained within biofuels. In this study, the Taguchi method, analysis of variance (ANOVA), and artificial neural network (AI-ANN) are utilized to predict the SCB of biofuels from the spent mushroom substrate (SMS) torrefaction via microwave-assisted heating (MAH). In Taguchi’s orthogonal array, washing, catalyst, and power operating parameters are considered. Acid and water washings lower the SMS’s ash content by 58.45% and 36.29%, respectively. The optimum conditions for combing acid washing, a catalyst with higher Fe 2 O 3 (35%), and microwave power of 540 W render the highest total SCB in biofuels (biochar+bio-oil) of 47.90 MJ·kg -1 , which is close to the SCB of crude oil derivatives (41-49 MJ·kg -1 ). The enhancement of the biomass’s SCB value from optimal torrefaction approximately 3 folds (256.30%) from 12.84 (raw) to 45.75 MJ·kg -1 . The ANN model with an architecture of 1 hidden layer (sigmoid activation function) with 3 neurons and the output layer (piecewise linear activation function) with a quick propagation algorithm for the training process of all layers poses excellent predictions with high accuracy R 2 =1. This shows that ANN with the designed scheme is precise for predicting the SCB of SMS-derived biofuels. © 2022, The Authors. All rights reserved.

关键词： Taguchi methods

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Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment

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Computers in Biology and Medicine 2024年 179卷 108904-108904页

作者： Chen, Chi-Wei Liu, Wayne-Young Huang, Lan-Ying Chu, Yen-Wei Graduate Degree Program of Smart Healthcare & Bioinformatics I-Shou University Kaohsiung City Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung City Taiwan Doctoral Program in Medical Biotechnology National Chung Hsing University Taichung City Taiwan Department of Urology Jen-Ai Hospital Taichung City Taiwan Graduate Institute of Genomics and Bioinformatics National Chung Hsing University Taichung City Taiwan Institute of Molecular Biology National Chung Hsing University Taichung City Taiwan Agricultural Biotechnology Center National Chung Hsing University Taichung City Taiwan Rong Hsing Research Center for Translational Medicine Taichung City Taiwan Ph. D Program in Medical Biotechnology National Chung Hsing University Taichung City Taiwan Taichung402 Taiwan

Urinary tract stones are a common and frequently recurring medical issue. Accurately predicting the success rate after surgery can help avoid ineffective medical procedures and reduce unnecessary healthcare costs. This study collected data from patients with upper ureter stones who underwent extracorporeal shock wave lithotripsy, including cases of successful as well as unsuccessful stone removal after the first and second lithotripsy procedures, and constructed prediction systems for the outcomes of the first and second lithotripsy procedures. Features were extracted from three categories of information: patient characteristics, stone characteristics, and extracorporeal shock wave lithotripsy machine data, and additional features were created using Feature Creation. Finally, the impact of features on the models was analyzed using six methods to calculate feature importance. Our prediction model for the first lithotripsy, selected from among 43 methods and seven ensemble learning techniques, achieves an AUC of 0.91. For the second lithotripsy, the AUC reaches 0.76. The results indicate that the detailed and binary information provided by patients regarding their history of stone experiences contributes differently to the predictive accuracy of the first and second lithotripsy procedures. The prediction tool is available at https://***/ks. © 2024

关键词： Machine learning

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Extraction and characteristic of Dioscorea alata mucilage 3

Extraction and characteristic of Dioscorea alata mucilage

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3rd International Conference on Agricultural engineering for Sustainable Agriculture Production, AESAP 2019

作者： Fortuna, D. Mardjan, S.S. Sunarti, T.C. Darmawati, E. Widayati, S.M. Purwanti, N. Doctoral Program in Agricultural Engineering Science Department of Mechanical and Biosystem Engineering Bogor Agricultural University Bogor Indonesia Department of Mechanical and Biosystem Engineering Bogor Agricultural University Bogor Indonesia Department of Agricultural Industry Technology Bogor Agricultural University Bogor Indonesia Bogor Indonesia

Dioscorea alata (DA) mucilage is classified as water-soluble polysaccharides, which has potential as a source of hydrocolloid. This research was conducted to investigate effects of salt types on water to tuber ratio to produce the most optimal mucilage yield. This research was conducted using Completely Randomized Factorial Design 3 x 5. The DA mucilage was extracted using two factors, i.e., types of salt (without salt, sodium chloride, and calcium chloride) and water to tuber ratios (2:1, 4:1, 6:1, 8:1, and 10:1). The results showed that the types of salt significantly affect (P © Published under licence by IOP Publishing Ltd.

关键词： Calcium chloride

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Limocitrin induced cellular death through ERK pathways in human oral squamous cell cancer

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Scientific Reports 2025年第1期15卷 1-10页

作者： Velmurugan, Bharath Kumar Lin, Chia-Chieh Kao, Min-Yun Ho, Hsin-Yu Lo, Yu-Sheng Chuang, Yi-Ching Hsieh, Ming-Ju Maitocon Laboratory Tirupur 641604 India Oral Cancer Research Center Changhua Christian Hospital Changhua 50006 Taiwan Graduate Institute of Clinical Medicine College of Medicine National Chung Hsing University Taichung 402202 Taiwan Doctoral Program in Tissue Engineering and Regenerative Medicine College of Medicine National Chung Hsing University Taichung 402 Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung 404 Taiwan

This study sought to investigate the anticancer efficacy of limocitrin on two distinct human oral cancer cell lines. At first, we evaluated the effect of limocitrin on the proliferation of OSCC cells (SCC-9 and SCC-47) using MTT and colony formation assays. Limocitrin treatment increased cell cycle arrest at G2/M phase, induced caspase-related apoptosis (cleaved caspase-3, caspase-8, caspase-9 and PARP expression) in OSCC cells. Limocitrin treatment inhibited Bcl-2 and Bcl-XL and induced Bax, Bak expression in both SCC-9 and SCC-47 cell lines. Limocitrin treatment inhibited cyclin E1, E2, CDK2, CDK4, and CDK6 and increased p21 expression. Limocitrin also exhibited an inhibitory effect on the phosphorylation of AKT, ERK1/2 and JNK in a dose-dependent manner. Additionally, pretreatment of oral cancer cells with U0126 resulted in increased cleaved caspase-3 and caspase-8 and PARP expression. Furthermore, limocitrin treatment decreased XIAP, cIAP1, HSP27 protein expression than control group. Combined treatment with limocitrin and si-XIAP significantly increased cleaved PARP, caspase-3 and -8 expressions in SCC-9 cells. Overall, this evidence indicates that limocitrin may serve as an effective anticancer agent for the treatment of oral cancer. © The Author(s) 2025.

关键词： AKT Apoptosis ERK JNK Limocitrin Mitochondria OSCC

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Sustainable Bio-Adsorbent Generated from Coffee Waste for Dual Application in Heavy Metal and Dye Removal

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Processes 2025年第5期13卷 1364-1364页

作者： Jia-Yin Lin Pei-Tzu Chang Jun-Ren Shi Fu-Chen Liu Chih-Ying Wang Nai-Wen Tsao Master and Doctoral Program in Industrial and Smart Technology Academy of Circular Economy National Chung Hsing University Nantou 540 Taiwan Master and Doctoral Program in Semiconductor and Green Technology Academy of Circular Economy National Chung Hsing University Nantou 540 Taiwan Department of Environmental Engineering and Science Chia Nan University of Pharmacy and Science Tainan 717 Taiwan Master and Doctoral Program in Specialty Crops and Metabolomics Academy of Circular Economy National Chung Hsing University Nantou 540 Taiwan

Heavy metal and dye contamination from industrial wastewater present substantial dangers to both ecological systems and human well-being. This study explores the upcycling of Coffee Powder Trimmings (CPT), a biomass waste rich in oxygen-containing functional groups, for water remediation. CPT was first used to adsorb Cu2+ and Fe3+ ions, then pyrolyzed at 750 °C to form metal oxide biochar composites (Cu/CB and Fe/CB). Characterization confirmed the formation of CuO and Fe3O4 particles and the retention of key adsorption functionalities. The materials were evaluated for methylene blue (MB) removal across pH levels, various water bodies, and multiple reuse cycles. CPT effectively removed >95% of Cu2+ and Fe3+ via chelation, while Fe/CB achieved up to 97.8% MB removal due to synergistic π–π, hydrogen bonding, and coordination interactions. Both biochars retained high performance after five cycles, with Fe/CB maintaining 86.88% efficiency. These results highlight CPT-derived biochar as a sustainable, low-cost adsorbent for dual removal of heavy metals and dyes.

关键词： Coffee Powder Trimmings heavy metal dye removal

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Theory-guided randomized neural networks for decoding medication-taking behavior

Theory-guided randomized neural networks for decoding medica...

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2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021

作者： Kaur, Navreet Gonzales, Manuel Alcaraz, Cristian Garcia Barnes, Laura E. Wells, Kristen J. Gong, Jiaqi Department of Engineering Systems and Environment University of Virginia VA22904 United States SDSU UC San Diego Joint Doctoral Program in Clinical Psychology San DiegoCA92120 United States Department of Psychology San Diego State University San DiegoCA92182 United States Department of Computer Science The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9781665403580

Long-term endocrine therapy (e.g. Tamoxifen, aromatase inhibitors) is crucial to prevent breast cancer recurrence, yet rates of adherence to these medications are low. To develop, evaluate, and sustain future interventions, individual-level modeling can be used to understand breast cancer survivors' behavioral mechanisms of medication-taking. This paper presents interdisciplinary research, wherein a model employing randomized neural networks was developed to predict breast cancer survivors' daily medication-taking behavior based on their survey data over three time periods (baseline, 4 months, 8 months). The neural network structure was guided by random utility theory developed in psychology and behavioral economics. Comparative analysis indicates that the proposed model outperforms existing computational models in terms of prediction accuracy under conditions of randomness. © 2021 IEEE

关键词： Diseases

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Communal-based domestic rainwater harvesting system: A novel approach to alternative solutions for increasing water supply and recharging groundwater in Jagakarsa urban area, South Jakarta

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Suprapti, Suprapti Kusuma, Muhammad Syahril Badri Kardhana, Hadi Cahyono, Muhammad Juliana, Imroatul Chalimah Doctoral Program of Civil Engineering Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Jl. Ganesha No. 10 Jawa Barat Bandung 40132 Indonesia Water Resources Engineering Group Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Jl. Ganesha No. 10 Jawa Barat Bandung 40132 Indonesia Civil Engineering Department Faculty of Engineering and Sciences Ibnu Khaldun Bogor University Jl. Sholeh Iskandar Km. 2 Jawa Barat Kota Bogor 16162 Indonesia Research Center for Ecohydrology Ecohydraulics and Engineering Science Bandung Jl. Babakan Jeruk 1 No. 100 Jawa Barat Bandung 40132 Indonesia Civil Engineering Study Program Sriwijaya University Jl. Raya Lintas Timur Palembang - Prabumulih No. Km. 32 Indralaya Indah South Sumatra Indralaya Indonesia Study Program of Civil Engineering Faculty of Engineering Universitas Mercu Buana Jl. Meruya Selatan No. 01 Kembangan-Jakarta Barat Jakarta 11650 Indonesia

This study proposes a new approach, a communal-based domestic rainwater harvesting (CDRWH) system, which is a new model that combines domestic and communal rainwater harvesting systems. This model is an alternative solution to improve system performance and utilize rainwater overflow from the system. Rainwater runoff from several domestic tanks is channeled to a communal tank to be collected, processed, and redistributed to needy domestic tanks. Overflow from the communal tank is used for groundwater recharge. The CDRWH system model aims to increase water supply, reduce runoff, and conserve groundwater. The model simulation uses daily water balance analysis and yield before spillage algorithm. The simulation scenario uses variations in roof area, number of house occupants, storage tank volume, and rainfall categories: 20 years, wet year, normal year, and dry year. Jagakarsa, an urban area in South Jakarta, was selected for the CDRWH system modeling. The results showed that the CDRWH model significantly improved the performance of the volumetric reliability (VR) and time reliability (TR) systems. In addition, the model could show a comparison of total groundwater recharge (GWR) and groundwater extraction (GWE), where the GWR capacity was higher. This allows centralized rainwater management in communal tanks, thereby reducing groundwater usage. The design of this model is simple. Therefore it can be easily duplicated for application in other locations. © 2025 The Authors

关键词： Communal-based domestic rainwater harvesting (CDRWH) Groundwater extraction (GWE) Groundwater recharge (GWR) Jagakarsa-South Jakarta Reduced runoff Water supply

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