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Potassium hydroxide microwave-assisted pretreatment and simultaneous saccharification fermentation of banana trunk (Musa paradisiaca) for bioethanol production

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Wati, Luki Anugrah Anggriani, Ulfa Meila Andara, Dea Atikah, Putri Sufra, Rifqi Melwita, Elda Novia, Novia Department of Chemical Engineering Faculty of Engineering Universitas Sriwijaya Jl. Raya Palembang- Prabumulih KM 32 Inderalaya Ogan Ilir (OI) Sumatera Selatan 30662 Indonesia Department of Chemical Engineering Faculty of Production and Industrial Technology Institut Teknologi Sumatera Lampung Indonesia Master Program of Environmental Management Universitas Sriwijaya Jl. Padang Selasa 524 Bukit Besar Sumatera Selatan Palembang 30139 Indonesia

Lignocellulosic agricultural wastes, such as banana trunks, are known to have the potential to be converted into bioethanol. This study aimed to investigate the potassium hydroxide microwave-assisted pretreatment (PhMa) of the banana trunk (Musa paradisiaca) and Simultaneous Saccharification Fermentation (SSF) to produce bioethanol. The study procedures were performed with varying temperatures (55–95 °C) and durations (10–50 min). PhMa was conducted with microwave irradiation at 360 Hz. The SSF process was carried out at 30–40 °C for 4–96 h. The lowest lignin content was approximately 6.84 % at 95 °C for 50 min. The highest bioethanol yield was at 5 % (v/v) during 72 h of fermentation at 30 and 40 °C. PhMa pretreatment method enabled the determination of kinetic parameters by fitting a model to various experimental data. A first-order reaction kinetic was used to calculate the activation energy. The findings indicated the activation energy 19.67 kJ mol⁻¹. © 2025 The Authors

关键词： Banana trunk Bioethanol Delignification kinetic Microwave irradiation Simultaneous saccharification fermentation

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Analysis of Maturity Level of the management System in Hotel Sector Companies

Analysis of Maturity Level of the Management System in Hotel...

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International Conference on Tourism, Technology and Systems, ICOTTS 2020

作者： Muñoz, Alexander Parody Lara, Malory Beatriz Guerra Lopesierra, Wilfrido Montes Morales, Bulmaro Fuentes Santana, Miguel Industrial Engineering Program Universidad Libre Seccional Barranquilla Barranquilla Colombia Master in Integrated Management Systems Bogota Colombia Universidad de La Guajira Riohacha La Guajira Colombia PhD in Information Technology Integration in Organizations Barranquilla Colombia Universidad de La Costa Barranquilla Colombia

ISBN: (纸本)9789813342590

The main purpose of this research is to evaluate the impact of maturity in quality management systems on assets, income and current ratio in companies of the hotel sector in the city of Barranquilla and its metropolitan area, based on the analysis of the level of maturity of the system and the application of multivariate statistical models. To achieve this, the Business management strategies of this sector were characterized, based on a structured model of variables and based on field data referring to a representative sample of companies in the sector (n = 64). With this information, the determining factors in the level of maturity of the quality management systems were identified, which allowed to know if the processes developed in a management system, such as these, in the Hotel Sector, impact positively or negatively on the performance of the sector’s companies based on assets, income and the current ratio by means of a multiple regression model, since, although these processes are being implemented, there are no current studies that evidence the contributions to the performance of the sector’s companies. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality control

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Social media-based online entrepreneurship approach on millennials: A measurement of job pursuit intention on multi-level marketing

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Procedia Computer Science 2022年 197卷 110-117页

作者： Reny Nadlifatin Satria Fadil Persada Mutiara Clarinda Gogor Arif Handiwibowo Rr Ratih Laksitowati Yogi Tri Prasetyo Anak Agung Ngurah Perwira Redi Department of Information System Institut Teknologi Sepuluh Nopember Campus ITS Sukolilo Surabaya 60111 Indonesia Department of Business Management Institut Teknologi Sepuluh Nopember Campus ITS Sukolilo Surabaya 60111 Indonesia Department of Technology Management Institut Teknologi Sepuluh Nopember Campus ITS Sukolilo Surabaya 60264 Indonesia School of Industrial Engineering and Engineering Management Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Industrial Engineering Department BINUS Graduate Program-Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

The present research explores the behavioral perspective of the millennial generation on job pursuit intention on Multi-Level Marketing companies through social media. Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) are used to assess the job pursuit intention. The data were analyzed by Structural Equation Model (SEM). Analysis reveals the strong influence of attitude on job pursuit intention. The result in this research can be used as the theoretical basis that can be very beneficial for MLM companies, investors, and academicians in marketing. Further theoretical and practical suggestions are discussed.

关键词： Millennial MLM social media TPB TAM

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Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

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Heliyon 2023年第8期9卷 e19281页

作者： Liang, Chen-Wei Chang, Chia-Chun Hsiao, Chun-Yun Liang, Chen-Jui Department of Biomechatronic Engineering National Ilan University Yilan Taiwan Master Program in UAV Application and Smart Agriculture National Ilan University Yilan Taiwan Department of Environmental Engineering and Science Feng Chia University Taichung Taiwan International School of Technology and Management Feng Chia University Taichung Taiwan Artificial Intelligence Technology and Application Bachelor Program Feng Chia University Taichung Taiwan

Scattering visiometers are widely used to measure atmospheric visibility;however, visibility is difficult to measure accurately because the extinction coefficient decays exponentially with visual range according to the Koschmid's law. Moreover, models for predicting visibility are lacking due to the lack of accurate visibility observations to verify. This study formulated an artificial intelligence method for measuring atmospheric visibility in five topographical regions: hills, basins, plains, alluvial plains, and rift valleys. Four air pollution factors and five meteorological factors were selected as independent variables for predicting visibility by using three artificial intelligence models, namely a support vector machine (SVM) model, a multilayer perceptron (MLP) model, and an extreme gradient boosting (XGBoost) model. The GridSearchCV function was used to automatically tune model hyperparameters to determine the optimal parameter values of the three models for the five target areas. The predictions of the aforementioned three models underwent considerable considerably scale shrinking relative to observed values. The inappropriately low predicted visibility values might have been caused by the use of inaccurate observations for training. To solve this problem, formulas of scale ratio and downshift were used to adjust the predicted values. Statistical measurements of model performance measures by five quantitative methods (e.g., correlation coefficient, mean absolute error) showed that adjusted predictions were in strong agreement with the observation data for the five target areas. Therefore, the adjusted prediction has high reliability. Because of obvious differences in the topography, weather, and air quality of the five target areas, different models provided optimal predictions for different areas. In densely populated western Taiwan, the MLP model is most suitable for predicting visibility on hills whereas the XGBoost model is most suitable for predictin

关键词： Air pollutants Extinction coefficient Fog-haze Koschmieder's law Machine learning Terrain effects

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Low-to-Moderate Arsenic Exposure and Urothelial Tract Cancers with a Long Latent Period of Follow-Up in an Arseniasis Area

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Journal of Epidemiology and Global Health 2023年第4期13卷 807-815页

作者： Liao, Pei-Ju Lee, Chih-Hung Wang, Shu-Li Chiou, Hung-Yi Chen, Chien-Jen Seak, Chen-June Wu, I-Wen Hsu, Kuang-Hung International Program of Health Informatics and Management Chang Gung University Taoyuan Taiwan Master Degree Program in Health and Long-Term Care Industry Chang Gung University Taoyuan Taiwan Division of Nephrology Chang Gung Memorial Hospital Keelung Taiwan Department of Dermatology Kaohsiung Chang Gung Memorial Hospital Kaohsiung Taiwan National Institute of Environmental Health Sciences National Health Research Institutes Miaoli Taiwan Institute of Population Health Sciences National Health Research Institutes Zhunan Miaoli County Taiwan Master Program in Applied Epidemiology College of Public Health Taipei Medical University Taipei Taiwan Genomics Research Center Academia Sinica Taipei Taiwan Department of Emergency Medicine New Taipei Municipal Tucheng Hospital New Taipei City Taiwan Department of Emergency Medicine Lin-Kou Medical Center Chang Gung Memorial Hospital Taoyuan Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Laboratory for Epidemiology Department of Health Care Management and Healthy Aging Research Center Chang Gung University No. 259 Wen-Hwa 1St Road Kwei-Shan Taoyuan 333 Taiwan Research Center for Food and Cosmetic Safety College of Human Ecology Chang Gung University of Science and Technology Taoyuan Taiwan Department of Safety Health and Environmental Engineering Ming Chi University of Technology New Taipei City Taiwan

Background: Arsenic exposure can cause adverse health effects. The effects of long-term low-to-moderate exposure and methylations remain unclear. Objective: This study aims to examine the association between low-to-moderate arsenic exposure and urothelial tract cancers while considering the effects of methylation capacity. Methods: In this study, 5,811 participants were recruited from an arseniasis area in Taiwan for inorganic arsenic metabolite analysis. This follow-up study was conducted between August 1995 and December 2017. We identified 85 urothelial tract cancers in these participants, including 49 bladder and 36 upper urothelial tract cancer cases. A Cox proportional hazards model was employed. Results: The analyses revealed a significant association between concentrations of inorganic arsenic in water > 100 ug/L and bladder cancer occurrence, with a hazard ratio (HR) of 4.88 (95% CI 1.35–17.61). A monotonic trend was observed between concentrations of inorganic arsenic in water (from 0 to > 100 ug/L) and the incidence of urothelial tract cancer, including bladder cancer (p < 0.05) and upper urothelial tract cancers (p < 0.05). Participants with a lower primary methylation index or higher secondary methylation index had a prominent effect. Conclusions: Rigorous regulations and active interventions should be considered for populations with susceptible characteristics. © 2023, The Author(s).

关键词： Bladder cancer Dose–response relationship Long latency arsenic exposure Methylation capacity Upper urothelial tract cancers

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Experimental investigation on interlocking concrete block for masonry wall of non-engineered earthquake resistant buildings 4

Experimental investigation on interlocking concrete block fo...

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4th International Conference on Earthquake engineering and Disaster Mitigation, ICEEDM 2019

作者： Teguh, Mochamad Rivai, Furqon Widi Rahmyanti, Novi Pradana, Erik Wahyu Department of Civil Engineering and Disaster Risk Management Islamic University of Indonesia Yogyakarta Indonesia Master Program Department of Civil and Environmental Engineering Gadjah Mada University Yogyakarta Indonesia Department of Civil Engineering Sebelas Maret University Surakarta Indonesia

Most of the seismic damage of existing traditional buildings is due to the absence of practical beam and column structures as the main reinforcement of the building. While a masonry wall as a structural component is commonly negligible due to their relatively low strength in contributing to the frame structure. As a result, when the earthquake struck, the building collapsed, and the ruins of building elements hit the occupants seriously and caused many deaths. This paper presents the results of preliminary research on the experimental investigation of interlocking concrete block for the masonry wall applied to non-engineered earthquake-resistant buildings. The interlocking between concrete blocks is expected to contribute to the strength of the masonry wall in resisting the loads, either in-plane or out-of-plane directions. The novelty of this type of concrete block lies in the uniqueness of the interlocking shape, making it effective in withstanding the earthquake load. This research focuses on the testing of interlocking concrete block units in withstanding loads in the direction and perpendicular to the field and equipped with testing the compressive strength of the wall and diagonal shear strength. The results produce interlocking models of concrete block contribute to better strength than ordinary clay bricks for the masonry wall. © 2020 The Authors, published by EDP Sciences.

关键词： Walls (structural partitions)

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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Science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Using LSTM and Regression Analysis for Railway Passenger Traffic Volume Prediction

Using LSTM and Regression Analysis for Railway Passenger Tra...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Fu Jie Tey Chih-Chi Tuan Cheng-En Cai Ming-Xuan Wu Yun-Sheng Wu Tin-Yu Wu Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan

ISBN: (纸本)9781665492331

The purpose of this paper is to investigate the factors affecting the passenger traffic volume on the high-capacity MRT, to analyze the traffic volume trends, and to make traffic volume predictions more accurately. This paper uses the LSTM (Long Short-term Memory) model and regression analysis for Taipei MRT passenger traffic volume prediction. The training data, including dates, temperatures and number of passengers, were collected, and a regression analysis was performed to find trends in the data.

关键词： Temperature distribution Solid modeling Government Transportation Training data Predictive models Market research

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Strategy for evaluation, characterization and adoption of immersive technologies to the public of the tourism industry in Maule Region

Strategy for evaluation, characterization and adoption of im...

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CHILEAN Conference on Electrical, Electronics engineering, Information and Communication Technologies (CHILECON)

作者： Yazmina Stappung Felipe Besoain Institute of Biological Science and Master’s Student in Technology Management Program University of Talca Chile Engineering of Videogames and Virtual Reality School and The Mauletec Laboratory University of Talca Chile

ISBN: (纸本)9781665408745

During the last few years, virtual reality technology has been introduced significantly in different economic sectors, even in the most conservative, such as tourism. Tourists have evolved; now there are Tourists 4.0, those who plan their trips using digital resources, seeking uniqueness and to enhance the emotions that a travel experience gives. Thus, there are users who are willing to explore innovative technologies, such as augmented reality. These immersive experiences open a window to bring the tourist closer to a possible destination and finalize the trip. The simplification of the process is the key to adding followers, and also understanding who the final user will be, connecting users to a new tourist destination through the adoption of this technology. In this context, a strategy is proposed that will characterize the user, their reception and evaluation of the technological adoption of local technological products developed with immersive technologies for tourists in the Maule Region. This study will allow the incorporation of orientation, experiences, and expectations, through the application of different instruments, to identify the users who adopted the technology and defined the travel process through an immersive reality experience.

关键词： Economics Instruments Conferences Tourism industry Information and communication technology Augmented reality IEEE Regions

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

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