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On the maximum of cotangent sums related to the Riemann hypothesis in rational numbers in short intervals

Moscow Journal of Combinatorics and Number Theory 2022年第4期10卷 303-313页

作者： Maier, Helmut Rassias, Michael Th. Department of Mathematics University of Ulm Ulm Germany Department of Mathematics and Engineering Sciences Hellenic Military Academy Athens Greece Moscow Institute of Physics and Technology Dolgoprudny Russian Federation Program in Interdisciplinary Studies Institute for Advanced Study Princeton NJ United States

Cotangent sums play a significant role in the Nyman–Beurling criterion for the Riemann hypothesis. Here we investigate the maximum of the values of these cotangent sums over various sets of rational numbers in short ... 详细信息

关键词： Cotangent sums Estermann’s zeta function Kloosterman sums Nyman–Beurling criterion Riemann hypothesis Riemann zeta function

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I.T. Professionals' Preference for Work From Home Setup: A Conjoint Analysis Approach 22

I.T. Professionals' Preference for Work From Home Setup: A C...

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8th International Conference on e-Society, e-Learning and e-Technologies, ICSLT 2022

作者： Praga, Dhan Joseph P. Prasetyo, Yogi Tri Nadlifatin, Reny Persada, Satria Fadil Chuenyindee, Thanatorn School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok Thailand

ISBN: (纸本)9781450396660

Due to constant growth of technology it results to increase efficiency to work remotely, as a result, there is an increase in percentage of I.T. professionals working from home. This study used Conjoint Analysis in order to determine the most preferred work from home setup attributes by I.T. professionals. Using the attributes such as having a dedicated workspace, type of internet connection, type of communication tool, audio tool, accessories for their computer or laptop, type of chair, and frequency of team socials. Using conjoint analysis with orthogonal design, this study was able to evaluate the preference among I.T. professionals. The result of the study showed that the type of Internet Connection is the most important (30.975%). Next is the preference on dedicated workspace (22.633%), the type of accessories (13.059%), Communication tool (10.982%), frequency of team socials (9.152%), type of chairs (6.723%), least is the type of audio tools (6.477%). The result of this study could be used by the employers to improve the work from home conditions of their employees. © 2022 Association for Computing Machinery.

关键词： Statistical methods

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Association of the Factors Affecting Risk Score in HIRADC Form of a Thermal Power Plant 24

Association of the Factors Affecting Risk Score in HIRADC Fo...

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6th International Conference on Management Science and Industrial engineering, MSIE 2024

作者： Uayan, Christian Jay A. Prasetyo, Yogi Tri Novendah, Ierenne Ayuwati, Irene Dyah Cahigas, Maela Madel L. Nadlifatin, Reny School of Industrial and Engineering Management Mapúa University 658 Muralla St. Intramuros Manila1002 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila1002 Philippines International Bachelor Program in Engineering Yuan Ze University 135 Yuan-Tung Road Chung-Li32003 Taiwan Department of Industrial Engineering and Management Yuan Ze University 135 Yuan-Tung Road Chung-Li32003 Taiwan University of Surabaya Surabaya60284 Indonesia School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia

ISBN: (纸本)9798400717130

In devising an effective Hazard Identification, Risk Assessment and Determining Controls plan of a company, it is crucial to understand the factors that relate with the Inherent and Residual Risks to prioritize the implementation of controls. In this study, the Process Area, Activity, Hazard Category, Inherent Risk Score and Residual Risk Score are brought into Chi-Squared Test to identify which factors are closely associated with Inherent and Residual Risk Scores. Moreover, the factors and range of scores were assessed into categorical level through Phi coefficient analysis. Chi-Squared Test dictates that there is a close relationship between Process Area and the Inherent Risk Score while Activity is closely associated with the Residual Risk Score. Results suggest that the thermal power plant is putting more effort into activities that involve high-level risks especially those that directly affect the environment. © 2024 ACM.

关键词： Thermoelectric power plants

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Teaching Control Theory using Mobile Robot Obstacle Following/Avoidance with CoppeliaSim and MFO Algorithm.

Teaching Control Theory using Mobile Robot Obstacle Followin...

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IEEE Latin American Robotics Symposium, LARS

作者： M. A. Pastrana J. Bautista Jose Mendoza-Peñaloza L. H. Oliveira D. A. Mendes Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Engineering Systems Undergraduate Program Florencia University of the Amazonia Caquetá Colombia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

The advancements in information and communication technologies have had a significant impact on the engineering educational system. Virtual laboratories are progressively being adopted to improve the way in which students interact with simulations for control systems. The enhancement of visualization and interaction offered by modern computers presents an opportunity to teach the theoretical foundation with a more organic approach. In addition, there are optimization algorithms that can be employed to designing controllers in an optimal way without having extensive knowledge in the area of control theory. This paper delineates the utilization of CoppeliaSim software, the Moth Flame Optimization (MFO) algorithm, and the EVA mobile robot for teaching control theory with Single-Input, Single-Output systems (SISO), for mobile robot obstacle following/avoidance application. The approach employs an online multi-language (Spanish and Portuguese) methodology for students without knowledge of control theory.

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Near room-temperature ferromagnetism from double exchange in the van der Waals material CrGeTe3: Evidence from optical conductivity under pressure

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Physical Review B 2025年第14期111卷 L140402-L140402页

作者： Jihaan Ebad-Allah Daniel Guterding Meera Varma Mangesh Diware Shraddha Ganorkar Harald O. Jeschke Christine A. Kuntscher Experimentalphysik II Institute for Physics University of Augsburg 86135 Augsburg Germany Department of Physics Tanta University 31527 Tanta Egypt Technische Hochschule Brandenburg Magdeburger Straße 50 14770 Brandenburg an der Havel Germany Advanced Research Division Park Systems Co. Suwon 16229 Republic of Korea School of Mechanical Engineering Sungkyunkwan University 2066 Seobu-ro Jangan-gu Suwon Gyeonggi-do 16419 Republic of Korea Research Institute for Interdisciplinary Science Okayama University Okayama 700-8530 Japan

The unexpected discovery of intrinsic ferromagnetism in layered van der Waals materials has sparked interest in both their fundamental properties and their potential for novel applications. Recent studies suggest near room-temperature ferromagnetism in the pressurized van der Waals crystal CrGeTe3. We perform a comprehensive experimental and theoretical investigation of magnetism and electronic correlations in CrGeTe3, combining broad-frequency reflectivity measurements with density functional theory and dynamical mean-field theory calculations. Our experimental optical conductivity spectra trace the signatures of developing ferromagnetic order and of the insulator-to-metal transition (IMT) as a function of temperature and hydrostatic pressure. With increasing pressure, we observe the emergence of a midinfrared feature in the optical conductivity, indicating the development of strong orbital-selective correlations in the high-pressure ferromagnetic phase. We find a distinct relationship between the plasma frequency and Curie temperature of CrGeTe3, which strongly suggests that a double-exchange mechanism is responsible for the observed near room-temperature ferromagnetism. Our results clearly demonstrate the existence of a charge-transfer gap in the metallic phase, ruling out its previously conjectured collapse under pressure.

关键词： Van der Waals forces

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS 15

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BAS...

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15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information systems, MCCSIS 2022

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

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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STUDY ON ROBUST DESIGN FOR UNDERWATER IDENTIFICATION OF MULTI-FUNCTIONAL SMALL SURFACE VEHICLES

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Journal of Taiwan Society of Naval Architects and Marine Engineers 2022年第3期41卷 131-139页

作者： Lin, C.-C. Hung, C.-H. Huang, S.-H. Chang, K.-C. Department of Power Vehicle and Systems Engineering CCIT National Defense University Taiwan Master Program of Naval Architecture and Ocean Engineering CCIT National Defense University Taiwan

Following with the age of technology, many advanced continuous developing the multi-functional small sur-face boats, and applied to the real-time monitoring of the near-shore environment, salvage and search operations, and marine disaster early warning and other livelihood purposes. The field of military operations includes intelligence reconnaissance, offshore reconnaissance and patrol, protecting the fishing vessels, and anti-terrorism tasks, anti-mine warfare and target ships for shooting training and other related military fields. In this study, the unmanned surface vehicle is the catamaran that equipped with single-beam sonar system, and used the Taguchi experimental design method, analysis of variance to discuss the effect of each controllable factor on the identification ability under water. The difference analysis is helpful to improve the underwater identification ability of unmanned surface vehicles. In addition, based on this study results, the best factor-level combination is A1B3 (such as low ship’s speed and located the sonar device near free-surface). To achieve the robust design, the dynamic navigation time of un-manned surface vehicles and enhance the underwater identification performance, it can provide reference for other units. © 2022 Taiwan Society of Naval Architects and Marine Engineers. All rights reserved.

关键词： Sonar

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Mining the Sentiment of Online Gaming from Social-Media: A Consumer Insight

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Procedia Computer Science 2024年 234卷 1069-1075页

作者： Reny Nadlifatin Satria Fadil Persada Gilbert Aldrich Gunawan Yogi Tri Prasetyo Michael Nayat Young Anak Agung Ngurah Perwira Redi Kukuh Lukiyanto Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia School of Industrial Engineering and Engineering & Management Mapua University 658 Muralla St. Intramuros Manila 1102 Philippines Industrial Engineering Department Faculty of Engineering and Technology Sampoerna University Jakarta 12780 Indonesia

The gaming industry has changed since advanced technologies and hardware specifications were able to create the possibility of gaming with connective network or known as online gaming. Some communities are accepting positively, and others are contradicted. Thus, there is an urgency to know the insight from their sentiment. The present research extracts 10 thousand sentiments at social media with sentiment text analysis. The text is interpreted into six based emotions namely anger, disgust, fear, joy, sadness, and surprise. The mining analysis by lexicon valance aware dictionary and sentiment reasoner (VADER) projects the Joy emotions as the domination result with 94.74%. Positive joy expression on online gaming is revealed by extracting the sentiment analysis. In total, ten thousand data were collected from the corpus, which resulted in the happy sentiments that were experienced.

关键词： Sentiment Social media Online gaming Text analysis

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Mental Status Identification for Infertile Patients Using Artificial Intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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Ensemble Deep Learning NARX for Estimating Time Series of Earthquake Occurrence

Ensemble Deep Learning NARX for Estimating Time Series of Ea...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Hapsoro Agung Nugroho Eka Yuni Astuty Aries Subiantoro Benyamin Kusumoputro Department of Electrical Engineering Research Center for Artificial Intelligence and Data Engineering Faculty of Engineering University of Indonesia Depok Indonesia Information Systems Study Program Faculty of Engineering Darma Persada University Jakarta Indonesia

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Predicting earthquake occurrences in time series data remains a challenging task in seismology. NARX (Nonlinear Autoregressive with eXogenous input) neural networks have recently shown promise for accurate predictions. While previous research has demonstrated NARX's effectiveness in estimating earthquake frequencies, the architecture still relies on several pre-determined parameters. These parameters include the number of hidden layers, number of neurons, and delay time. This study aims to investigate the effectiveness of Ensemble Deep Learning NARX neural networks in predicting time series of earthquakes. The proposed ensemble model integrates multiple NARX neural networks, each trained on a distinct subset of earthquake data. This approach aims to enhance prediction accuracy and bolster model robustness. The dataset consists of time series records detailing earthquake occurrence frequencies and magnitudes. The results show performance evaluation metrics in terms of Mean Square Error (MSE) values. For training frequency data, the MSE is 4.10e-26, and for testing, it is 6.05e-22. Regarding training magnitude data, the MSE is 2.86e-21 for training and 3.17e-19 for testing. This study makes a valuable contribution to the advancement of earthquake prediction techniques, underscoring the potential of Ensemble Deep Learning NARX neural networks for precise time series estimation in seismology.

关键词： Deep learning Training Time series analysis Earthquakes Seismology Estimation Predictive models

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