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Some tridiagonal determinants related to central delannoy numbers, the chebyshev polynomials, and the fibonacci polynomials

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UPB Scientific Bulletin, Series A: applied mathematics and Physics 2019年第1期81卷 123-136页

作者： Qi, F. Čerňanová, V. Semenov, Y.S. Institute of Mathematics Henan Polytechnic University Jiaozuo China College of Mathematics Inner Mongolia University for Nationalities Tongliao China College of Science Tianjin Polytechnic University China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Department of Mathematics and Computer Science Faculty of Education Trnava University Priemy-selná 4 Trnava918 43 Slovakia Applied Mathematics-1 Moscow State University of Railway Engineering Moscow127994 Russia

In the paper, the authors give a motivation from central Delannoy numbers to a tridiagonal determinant, find a generating function for the tridiagonal determinant, prove several formulas for the tridiagonal determinant, discuss the inverse of the tridiagonal matrix, connect the tridiagonal determinant with the Chebyshev polynomials, the Fibonacci numbers and polynomials, and the Cauchy product of central Delannoy numbers, derive several formulas for the tridiagonal determinant and the second kind Chebyshev polynomials, review computation of general tridiagonal determinants, present two new formulas for computing general tridiagonal determinants, and generalize central Delannoy numbers and their Cauchy product. © 2019, Politechnica University of Bucharest. All rights reserved.

关键词： Chebyshev polynomials

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Optimizing novel thermal energy storage systems: Enhancing melting efficiency with tubes, stands, and advanced machine learning techniques

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Journal of Energy Storage 2025年 124卷

作者： Almadhor, Ahmad Basem, Ali Singh, Pradeep Kumar Othman, Nashwan Adnan Samad, Sarminah Alhomayani, Fahad M. Abduvalieva, Dilsora Ali, H. Elhosiny Akram, Muhammad Afzal, Abdul Rahman Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Faculty of Engineering Warith Al-Anbiyaa University Karbala56001 Iraq Department of Mechanical Engineering Institute of Engineering & Technology GLA University U.P. Mathura281406 India Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Department of Management College of Business Administration Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia College of Computers and Information Technology Taif University Saudi Arabia Applied College Taif University Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent100070 Uzbekistan Physics Department Faculty of Science King Khalid University P.O. Box 9004 Abha Saudi Arabia Faculty of Design Informatics and Business University of Abertay Scotland Dundee United Kingdom Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia

The transition to renewable energy is crucial to meet growing global energy demands while minimizing environmental impact. Solar energy, a leading renewable source, faces limitations due to its intermittent nature, making energy storage systems essential for continuous supply. Thermal energy storage systems (TESSs) provide a compelling solution, especially by utilizing latent heat storage with phase change materials (PCMs), which efficiently store large amounts of energy. However, PCM-based systems suffer from low thermal conductivity and weak heat transfer performance. Numerous techniques to amend the thermal conductivity of PCMs, whether applied individually or together, show promise for improving the efficiency of TESSs. However, challenges like increased viscosity, decreased latent heat capacity, high costs, and complex manufacturing often limit their practical use. Thus, it is crucial to investigate more straightforward and more cost-effective solutions, like incorporating extended surfaces into the PCM container. In this study, a novel approach is taken to ameliorate the heat transfer surface area in TESSs by incorporating tubes and stands inside the PCM container rather than relying on conventional fins. A new compact TESS design was introduced, featuring fixed tubes supported by aluminum-made stands. Two artificial neural networks (ANN) models were trained with input parameters, including the length of the vertical stands, horizontal stands, and tilted stands, to anticipate the half-melting time and complete melting time of the PCM. In the end, two optimal designs of O-TESS1 and O-TESS2 were proposed by a single-objective optimization, focusing on minimizing the half and complete melting times of the PCM, respectively. A multi-objective optimization was also performed to balance the mentioned objectives. Using the generated data, a Pareto front and TOPSIS-selected designs were developed to visualize the trade-offs between these competing objectives. It took

关键词： Solar heating

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Comprehensive review of optimization strategies for phase change materials: Techniques, applications, and challenges in thermal storage systems

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International Communications in Heat and Mass Transfer 2025年 166卷

作者： Mohsen Izadi Ioan Pop Sabir Ali Shehzad Faris Alqurashi Mohamed H. Mohamed Ahmad Hajjar Ibrahim Mahariq Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam Mechanical Engineering Department Faculty of Engineering Lorestan University Khorramabad Iran Department of Applied Mathematics Babes-Bolyai University Cluj-Napoca Romania Department of Mathematics COMSATS University Islamabad Sahiwal 57000 Pakistan Department of Mechanical Engineering College of Engineering University of Bisha Bisha 61922 P.O. Box 551 Saudi Arabia Mechanical Engineering Dept. College of Engineering and Architecture Umm Al-Qura University P.O. 5555 Makkah Saudi Arabia Center for Environmental Intelligence and College of Engineering and Computer Science VinUniversity Hanoi Vietnam College of Engineering and Architecture Gulf University for Science and Technology Mishref Kuwait Najjad Zeenni Faculty of Engineering Al Quds University Jerusalem Palestine University College Korea University Seoul 02481 South Korea Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Applied Science Research Center Applied Science Private University Amman Jordan

Phase Change Materials (PCMs) are widely recognized for their potential in thermal energy storage systems due to their high latent heat capacity. However, their practical application is significantly hindered by low thermal conductivity, which limits the charging and discharging rates of energy. This review provides a comprehensive and critical synthesis of recent passive techniques developed to enhance the thermal performance of PCMs. These include finned structures, porous media, nanoparticle dispersion, geometrical modifications, and multi-PCM strategies. Quantitative comparisons show that the use of fins can reduce melting time by up to 65 %, while metallic porous matrices can improve thermal conductivity by over 500 %. Incorporation of nanoparticles has demonstrated up to 25 % enhancement in heat transfer rates, albeit with increased viscosity. Geometrical innovations and multi-PCM layering have enabled tailored thermal responses across temperature ranges, suitable for applications such as solar thermal systems and building-integrated energy storage. Furthermore, this review identifies trade-offs associated with each method, including design complexity, cost, and material compatibility, and presents a comparative performance table to guide selection based on system requirements. Hybrid enhancement strategies, such as nanoparticle-doped PCMs embedded in metal foams, are also proposed as a promising direction for future research. The review concludes with specific, forward-looking insights, highlighting opportunities for PCM-based solutions in solar desalination, cold-chain transport, and passive building cooling under real-world constraints.

关键词： Metal foams

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Towards deployment-centric multimodal AI beyond vision and language

arXiv

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arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

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HARDY INEQUALITIES FOR MAGNETIC p-LAPLACIANS

arXiv

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arXiv 2022年

作者： Cazacu, Cristian Krejčiřík, David Lam, Nguyen Laptev, Ari Faculty of Mathematics and Computer Science University of Bucharest 14 Academiei Street Bucharest010014 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy No.13 Calea 13 Septembrie Sector 5 Bucharest050711 Romania Department of Mathematics Faculty of Nuclear Sciences and Physical Engineering Czech Technical University in Prague Trojanova 13 Prague120 00 Czech Republic School of Science Memorial University of Newfoundland Corner Brook the Environment Grenfell Campus NL A2H5G4 Canada Department of Mathematics Imperial College London Huxley Building 180 Queen's Gate LondonSW7 2AZ United Kingdom Sirius Mathematics Center Sirius University of Science and Technology 1 Olympic Ave Sochi354340 Russia

We establish improved Hardy inequalities for the magnetic p-Laplacian due to adding nontrivial magnetic fields. We also prove that for Aharonov-Bohm magnetic fields the sharp constant in the Hardy inequality becomes strictly larger than in the case of a magnetic-free p-Laplacian. We also post some remarks with open problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic fields

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Slip and thermal effects on MHD fractional Jeffrey nanofluid flow

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Journal of Radiation Research and applied Sciences 2025年第3期18卷

作者： Shajar Abbas S. Saleem Maryam Fatima Ioan-Lucian Popa Mudassar Nazar Ghada Ragheb Elnaggar Rashid Jan Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan AS (Ayub Shah) Institute of Mathematics and Research Centre Multan Pakistan Department of Mathematics College of Science King Khalid University Abha 61413 Saudi Arabia Center for Artificial Intelligence (CAI) King Khalid University Abha 61421 Saudi Arabia Department of Computing Mathematics and Electronics “1 Decembrie 1918” University of Alba Iulia 15 510009 Alba Iulia Romania Faculty of Mathematics and Computer Science Transilvania University of Brasov Iuliu Maniu Street 50 500091 Brasov Romania Industrial and Systems Engineering Department College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Mathematics Saveetha School of Engineering (SIMATS) Thandalam 600124 Chennai Tamil Nadu India Department of Mathematics Khazar University AZ1096 Baku Azerbaijan

Nanofluids with superior thermal characteristics are increasingly important in radiation-influenced environments across industrial and biomedical domains. This work develops a fractional-order model describing the mixed convection flow of human blood-based nanofluids embedded with single-wall carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes (MWCNTs) along an infinite vertical surface. To capture memory and hereditary effects, the constant proportional Caputo (CPC) operator is utilized, while the Laplace technique yields semi-analytical solutions for temperature, velocity, and concentration profiles, and its inverse Laplace transform is obtained using the Stehfest and Tzou algorithm. The results show that thermal radiation significantly enhances heat transfer, with a 12 % increase in temperature for blood-SWCNT nanofluid due to superior thermal conductivity, while blood-MWCNT nanofluid exhibits higher velocity due to lower density. These findings contribute to the development and optimization of nanofluid systems for use in industrial engineering applications, including high-performance heat exchangers, solar collectors, electronic cooling systems, and biomedical thermal therapies where precise thermal diffusion control over is essential.

关键词： Thermal flow Laplace transform Carbon nanotubes MHD Jeffrey fluid

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MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing 24

MRP 2020: The Second Shared Task on Cross-Framework and Cros...

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Shared Task: Cross-Framework Meaning Representation Parsing at 24th Conference on Computational Natural Language Learning, CoNLL 2020

作者： Oepen, Stephan Abend, Omri Abzianidze, Lasha Bos, Johan Hajič, Jan Hershcovich, Daniel Li, Bin O’Gorman, Tim Xue, Nianwen Zeman, Daniel University of Oslo Department of Informatics Norway The Hebrew University of Jerusalem School of Computer Science and Engineering Israel Utrecht University UiL OTS Netherlands University of Groningen Center for Language and Cognition Netherlands Charles University Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic University of Copenhagen Department of Computer Science Denmark Nanjing Normal University School of Chinese Language and Literature China University of Massachusetts at Amherst College of Information and Computer Sciences United States Brandeis University Department of Computer Science United States

ISBN: (纸本)9781952148644

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization;for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://***. © 2020 Association for Computational Linguistics.

关键词： Directed graphs

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Trajectory Outlier Detection: Algorithms, Taxonomies, Evaluation, and Open Challenges

Trajectory Outlier Detection: Algorithms, Taxonomies, Evalua...

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作者： Belhadi, Asma Djenouri, Youcef Lin, Jerry Chun-Wei Cano, Alberto Kristiania University College Department of Technology Kirkegata 24 Oslo0153 Norway Department of Mathematics and Cybernetics SINTEF Digital Forskningsveien 1 Oslo0373 Norway Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Inndalsveien 28 Bergen5063 Norway Virginia Commonwealth University Department of Computer Science 401 W. Main St RichmondVA23284 United States

Detecting abnormal trajectories is an important task in research and industrial applications, which has attracted considerable attention in recent decades. This work studies the existing trajectory outlier detection algorithms in different industrial domains and applications, including maritime, smart urban transportation, video surveillance, and climate change domains. First, we review several algorithms for trajectory outlier detection. Second, different taxonomies are proposed regarding application-, output-, and algorithm-based levels. Third, evaluation of 10 trajectory outlier detection algorithms is performed on small, large, and big trajectory databases. Finally, future challenges and open issues with regard to trajectory outliers are derived and discussed. This survey offers a general overview of existing trajectory outlier detection algorithms in industrial informatics applications. As a result, mature solutions may be further developed by data mining and machine learning communities. © 2020 ACM.

关键词： Anomaly detection

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Noncommutative perspectives of operator monotone functions in Hilbert spaces

arXiv

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arXiv 2020年

作者： Dragomir, Silvestru Sever Mathematics College of Engineering & Science Victoria University PO Box 14428 Melbourne CityMC8001 Australia School of Computer Science & Applied Mathematics University of the Witwatersrand Private Bag 3 Johannesburg2050 South Africa

Assume that the function f: (0, ∞) → R is operator monotone in (0, ∞) and has the representation (formula presented), where a ∈ R, b ≥ 0 and w is a positive measure on (0, ∞). In this paper we obtained among others that (formula presented).Applications for weighted operator geometric mean and relative operator entropy are also *** Codes 47A63, 47A30, 15A60 Copyright © 2020, The Authors. All rights reserved.

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An eco-friendly multi-generation system integrating geothermal-driven power and cooling cycles with LNG regasification and hydrogen production: a thermo-economic optimization

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applied Thermal engineering 2025年 277卷

作者： Ahmad Almadhor Sarminah Samad Mohamed Shaban Azher M. Abed Fahad M. Alhomayani Dilsora Abduvalieva Salah Knani Amani Alruwaili Fahad Alturise Salem Alkhalaf Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Department of Management College of Business Administration Princess Nourah bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Physics Faculty of Science Islamic University of Madinah Madinah 42351 Saudi Arabia Air Conditioning and Refrigeration Techniques Engineering Department College of Engineering and Technologies Al-Mustaqbal University Babylon 51001 Iraq Al - Mustaqbal Center for Energy Research Al-Mustaqbal University Babylon 51001 Iraq College of Computers and Information Technology Taif University Saudi Arabia Applied College Taif University Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor Avenue 27 Tashkent 100070 Uzbekistan Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Physics College of Science Northern Border University Arar Saudi Arabia Department of Cybersecurity College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia

This study develops a multi-generation framework utilizing geothermal energy to produce electricity, hydrogen, and cooling with high efficiency. The system integrates a Kalina cycle (KC) and an organic Rankine cycle (ORC), supported by a thermoelectric generator (TEG) for waste heat recovery and a proton exchange membrane electrolyzer (PEME) for hydrogen production. Geothermal energy is leveraged to optimize resource utilization, while waste heat is repurposed for LNG regasification, achieving full vaporization and additional power generation. Unlike previous works, this study introduces a fully integrated and cascade-utilized configuration that combines multiple thermal subsystems, each optimized in synergy. This approach enhances energy recovery and reduces system-level exergy destruction, representing a unique advancement in geothermal-based multi-generation design. The results reveal that geothermal fluid temperature ( T GF ) is the most critical parameter influencing the system’s performance. Increasing T GF significantly enhances electricity, chilled water, and hydrogen production, improves exergy efficiency, and reduces levelized costs of electricity (LCOE) and hydrogen (LCOH). In contrast, a higher pinch point temperature difference (PPTD) at the KC evaporator reduces power and hydrogen output but increases chilled water production, revealing key trade-offs between thermal efficiency and operational outputs. Moreover, the LNG stream accounts for 40 % of the system’s total exergy loss, followed by the KC (20 %) and ORC (18 %), identifying areas for targeted efficiency improvements. Optimization results show that a turbine inlet pressure of 5900 kPa achieves the highest system efficiency at 22.74 %, while genetic algorithm (GA) optimization enhances second-law efficiency to 25.11 % with an operational cost rate of 137.54 $/h. These findings demonstrate the system’s ability to maximize energy recovery and improve overall cost-effectiveness, providing valuable i

关键词： Energy reuse Geothermal energy Hydrogen production Kalina cycle LNG regasification Techno-economic optimization

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