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Resource optimisation in aquaponics facility via process monitoring and graph-theoretical approach

Carbon Resources Conversion 2022年第4期5卷 255-269页

作者： Vojtˇech Ondruˇska Bing Shen How Michal Netolický Vítˇezslav M´aˇsa Sin Yong Teng Brno University of Technology Institute of Process Engineering&NETME CentreTechnick´a 2896/261669 BrnoCzech Republic Biomass Waste-to-Wealth Special Interest Group Research Centre for Sustainable TechnologiesFaculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang Tiga93350KuchingSarawakMalaysia Flenexa plus s.r.o. Pˇr´aslavice 33578354Pˇr´aslaviceOlomoucCzech Republic Radboud University Institute for Molecules and MaterialsP.O.Box 90106500 GL Nijmegenthe Netherlands

Energy efficiency and economic viability are the often-quoted issues in aquaponic *** work aims to(i)identify process technologies and technical measures which would enhance the profitability of aquaponics business while conserving energy and other resources,and(ii)to validate the determined optimal measures on the testing aquaponics *** process network synthesis technique was used to search for an optimal process pathway while the image processing technique was applied to automatically monitor the growth rate of produce since it is the main revenue stream in *** the aid of P-graph method,the optimal feasible structure has 9 times higher annual net income than that of the existing *** optimal solution includes the integration of electrical heat pump,biogas system,and utilizes black solider fly(BSF)facility to produce fish *** light energy savings were achieved by practical installation of reflective foils which improved 16.88%of Photosynthetic photon flux density(PPFD)on growth *** measures can help the aquaponics farms to become more competitive and to decrease their ecological footprint.

关键词： Aquaponics Process monitoring P-graph Process optimization Energy savings

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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Bornean Orangutan Nests Classification using Multiclass SVM

Bornean Orangutan Nests Classification using Multiclass SVM

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IEEE Symposium on Computers & Informatics (ISCI)

作者： Amanda Aiza Amran Chin Kim On Lai Po Hung Munirah Rossdy Donna Simon Chai Soo See Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Faculty of Computing and Informatics Creative Advanced Machine Intelligence (CAMI) Research Centre Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Faculty of Computer and Mathematical Science Universiti Teknologi MARA Kota Kinabalu Sabah Malaysia Orangutan Conservation WWF-Malaysia Kota Kinabalu Sabah Malaysia Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak

Bornean orangutans are critically endangered, and experts are working to manually spot and classify their nests. This is a challenging task, as it requires a deep understanding of orangutan behavior and their natural habitats. To address this challenge, researchers are developing advanced image analysis techniques that can minimize human intervention. In this paper, we propose a method for classifying orangutan nests using Support Vector Machines (SVMs). SVMs are a powerful machine learning algorithm that has been shown to be effective for image classification. Our method includes a comprehensive image pre-processing and enhancement pipeline, as well as a rigorous training and testing procedure. The results of our study show that SVMs can achieve high accuracy in classifying orangutan nests. We achieved an accuracy of 79.90%, a F1-score of 79.87%, a precision of 79.91%, and a recall of 79.82%. These results demonstrate the effectiveness of our method and the potential of SVMs for this task.

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Dynamic Analysis and Circuit Design of a New 3D Highly Chaotic System and its Application to Pseudo Random Number Generator (PRNG) and Image Encryption

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SN Computer science 2024年第4期5卷 420页

作者： Sambas, Aceng Benkouider, Khaled Kaçar, Sezgin Ceylan, Nurdoğan Vaidyanathan, Sundarapandian Sulaiman, Ibrahim Mohammed Mohamed, Mohamad Afendee Ayob, Ahmad Faisal Mohamad Muni, Sishu Shankar Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu Kuala 22200 Malaysia Department of Mechanical Engineering Universitas Muhamadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Department of Electronics Faculty of Technology Badji-Mokhtar University B.P. 12 Sidi Ammar Annaba 23000 Algeria Department of Electrical and Electronics Engineering Sakarya University of Applied Sciences Serdivan Sakarya 54050 Turkey Department of Mechanical Engineering Adıyaman University Merkez Adıyaman 02040 Turkey Centre for Control Systems Vel Tech University Tamil Nadu Chennai 600062 India School of Quantitative Science University Utara Malaysia Sintok Kedah Bukit Kayu Hitam 06010 Malaysia Faculty of Education and Arts Sohar University Sohar 311 Oman Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Gong Badak Terengganu Kuala Terengganu 21300 Malaysia Department of Physical Sciences Department of Physical Sciences Indian Institute of Science Education and Research Mohanpur Kolkata 741246 India

In this paper, a new 3D dynamical system with four quadratic nonlinear terms is presented. It is shown that the proposed chaotic system has two saddle-foci, unstable, equilibrium points. Thus, the proposed chaotic system exhibits self-excited chaotic attractors. Dynamical analysis methods such as Lyapunov exponents spectrum, bifurcation diagrams, and phase portraits are used to explore the complex dynamical behaviors of the proposed chaotic system and analyze its basic qualitative properties. It is shown that the maximal Lyapunov exponent (MLE) of the new chaotic system is 7.196, which is a high value. The proposed highly chaotic system exhibits high complexity and it will be very useful for applications in cryptography, encryption and secure communications. The physical feasibility of the proposed system is verified by implementing its electronic circuit schematic using Multisim software. Additionally, a PRNG has been developed using the derived state variables and analyze with NIST-800–22 test. Lastly, we developed a PRNG-based image encryption and employ it for an encryption. The experimental outcomes obtained prove that the 3D chaotic system-based encryption application presented in this section has a very good performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Chaos Image encryption Pseudo random number generator

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GPT-Based Automated Induction: Vulnerability Detection in Medical Software

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Deng, Liangjun Lei, Hang Khan, Fazlullah Srivastava, Gautam Chen, Jingxue Haque, Mainul University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China University of Nottingham Ningbo China School of Computer Science and the Next-Generation Internet of Everything Laboratory Faculty of Science and Engineering Zhejiang Ningbo315104 China Lovely Professional University Division of Research and Development Phagwara India Chandigarh University University Centre of Research and Development Mohali India Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India University of Nottingham Ningbo China Department of Mathematics Faculty of Science and Engineering Zhejiang Ningbo315104 China

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software. © 2025 IEEE. All rights reserved,

关键词： Scalability

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Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

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Intelligent Automation & Soft computing 2023年第6期36卷 3139-3155页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah22230Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based *** stay in the leading positions for business as well as personal *** popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email *** filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the *** prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully ***,this study provides a solution related to email message body text automatic classification into phishing and email *** paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email *** presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam *** the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further ***,the SRRNN method can be useful in recognizing and classifying spam *** hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter *** investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

关键词： Email classification applied linguistics improved fruitfly optimization deep learning recurrent neural network

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AI-Empowered Catalyst Discovery: A Survey from Classical Machine Learning Approaches to Large Language Models

arXiv

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arXiv 2025年

作者： Xu, Yuanyuan Wang, Hanchen Zhang, Wenjie Xie, Lexing Chen, Yin Salim, Flora Zhang, Ying Gooding, Justin Walsh, Toby School of Computer Science and Engineering The University of New South Wales Sydney Australia Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Computing Australian National University Canberra Australia Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Australian Centre for Nanomedicine The University of New South Wales Sydney Australia

Catalysts are essential for accelerating chemical reactions and enhancing selectivity, which is crucial for the sustainable production of energy, materials, and bioactive compounds. Catalyst discovery is fundamental yet challenging in computational chemistry and has garnered significant attention due to the promising performance of advanced Artificial Intelligence (AI) techniques. The development of Large Language Models (LLMs) notably accelerates progress in the discovery of both homogeneous and heterogeneous catalysts, where their chemical reactions differ significantly in material phases, temperature, dynamics, etc. However, there is currently no comprehensive survey that discusses the progress and latest developments in both areas, particularly with the application of LLM techniques. To address this gap, this paper presents a thorough and systematic survey of AI-empowered catalyst discovery, employing a unified and general categorization for homogeneous and heterogeneous catalysts. We examine the progress of AI-empowered catalyst discovery, highlighting their individual advantages and disadvantages, and discuss the challenges faced in this field. Furthermore, we suggest potential directions for future research from the perspective of computer science. Our goal is to assist researchers in computational chemistry, computer science, and related fields in easily tracking the latest advancements, providing a clear overview and roadmap of this area. We also organize and make accessible relevant resources, including article lists and datasets, in an open repository at https://***/LuckyGirl-XU/Awesome-Artificial-Intelligence-Empowered-Catalyst-Discovery. © 2025, CC BY-NC-SA.

关键词： Catalyst selectivity

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Bushfire Severity Prediction Optimization: Feature Combination Study with XGBoost Regression and Satellite-Derived Data

Bushfire Severity Prediction Optimization: Feature Combinati...

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Security of Information and Networks (SIN), International Conference on

作者： Pirunthavi Wijikumar Shouthiri Partheepan Jahan Hassan Farzad Sanati Biplop Rakshit Ray Department of ICT Faculty of Technological Studies University of Vavuniya Vavuniya Sri Lanka School of Engineering and Technology Central Queensland University Norman Gardens QLD Australia Department of Computing Faculty of Science Eastern University of Sri Lanka Vantharumoolai Chenkalady Batticaloa Sri Lanka Centre for Machine Learning Networking and Education Technology (CML-NET) College of Information and Communication Technology School of Engineering and Technology Central Queensland University Australia

ISBN: (数字)9798331509736

ISBN: (纸本)9798331509743

Bushfire severity prediction plays a vital role in mitigating the catastrophic effects of bushfires on ecosystems, human life, and property. Accurate prediction models enable timely interventions and informed decision-making, which are crucial for effective fire prevention and response strategies. This study aims to develop an efficient bushfire severity prediction model by leveraging satellite-derived data, specifically from Landsat 8, which includes spectral indices, topographical and climatic features. The primary objective of this study is to assess the impact of different feature combinations on model performance using the XGBoost regression model with dNBR as the target variable. Eighteen tests were conducted, ranging from single-feature indices like NDVI to more complex multi-feature sets. The findings revealed that a 21-feature combination achieved the highest accuracy of 90.09%, with a MSE of 0.0008, a computational time of 1201.86 seconds, and a memory usage of 106.28 MB. However, adding more features did not always result in proportional improvements and often led to increased computational demands. In contrast, tests with fewer features demonstrated that high precision could be achieved with reduced resource consumption, offering a favourable trade-off between performance and efficiency. The results underscore the importance of strategic feature selection and optimization to enhance model accuracy while minimizing computational over-head. This study provides valuable insights for creating effective, high-performance fire severity prediction models, especially in resource-constrained settings.

关键词： Accuracy Computational modeling Biological system modeling Memory management Decision making Predictive models Feature extraction Data models Computational efficiency Optimization

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DeepNNet 15 for the prediction of biological waste to energy conversion and nutrient level detection in treated sewage water

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Process Safety and Environmental Protection 2024年 189卷 636-647页

作者： Sathish, T. Vijayalakshmi, A. Surakasi, Raviteja Ahalya, N. Rajkumar, M. Saravanan, R. Shangdiar, Sumarlin Sithole, Thandiwe Amesho, Kassian T.T. Department of Mechanical Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Department of Chemistry R.M.K. Engineering College Thiruvallur District Tamil Nadu India Department of Mechanical Engineering Lendi Institute of Engineering and Technology Andhra Pradesh Vizianagaram India Department of Biotechnology MS Ramaiah Institute of Technology Karnataka Bangalore India Department of Smart Computing School of Information Technology and Engineering Vellore Institute of Technology VIT University Vellore Campus Tamil Nadu India Institute of Environmental Engineering National Sun Yat-Sen University Kaohsiung804 Taiwan Center for Emerging Contaminants Research National Sun Yat-Sen University Kaohsiung804 Taiwan The International University of Management Centre for Environmental Studies Main Campus Dorado Park Ext 1 Windhoek Namibia Department of Biotechnology Faculty of Life Science and Technology AKS University Satna Madhya Pradesh 485001 India Department of Chemical Engineering University of Johannesburg PO Box 17011 Doornfontein2088 South Africa

Efforts toward sustainable environmental development encompass global initiatives to enhance water quality, sanitation, and wastewater management. This study addresses the energy-intensive nature of sewage water treatment, which is critical for curbing water pollution. We propose a 15-layer Deep Neural Network (DeepNNet-15) to analyse energy conversion and nutrient levels. Constructed with dense layers of up to 10 neurons each, featuring ReLU activation, DeepNNet-15 utilises a benchmark sewage water dataset for training. With 11 input and five output neurons, including energy and nutrient parameters, DeepNNet-15 predicts parameters with less than 3 % error. Its deep learning performance demonstrates over 90 % accuracy, precision, and above 97 % specificity. The practical significance of this research lies in the demonstrated efficacy of DeepNNet-15 in forecasting energy conversion efficiency and nutrient levels in treated sewage water. By harnessing advanced neural network architecture and machine learning techniques, this study offers a tangible contribution to sustainable environmental practices. It equips stakeholders with a robust tool to enhance sewage water treatment efficiency and effectiveness. It aligns with global sustainable development goals and promotes pollution mitigation, resource optimisation, and a cleaner, healthier environment. © 2024 The Institution of Chemical Engineers

关键词： Conversion efficiency

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Towards Blockchain-based Decentralized Employee Transfer System

Towards Blockchain-based Decentralized Employee Transfer Sys...

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Blockchain computing and Applications (BCCA), International Conference on

作者： Alam Zaib Ehzaz Mustafa Ahsan Khan Hasan Ali Khattak Isa Ali Ibrahim Abdallah Namoun Junaid Shuja Raja Wasim Ahmad Department of Computer Science Comsats University Islamabad Pakistan Faculty of Computing & Information Technology Sohar University Sohar Oman School of Information and Communication Technology Federal University of Technology Owerri Imo Nigeria AI Centre Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia Department of Computer Science Southeast Missouri State University Cape Girardeau MO USA Department of Computer & Information Sciences Ajman University Ajman United Arab Emirates

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

Employees are the main driving force in organizations that undergo recruitment, promotion, and transfer. There are many employee management systems, but most of them are centralized. Centralization leads to problems such as lack of transparency, auditability issues, vulnerability, and a single point of failure. Blockchain technology addresses these concerns. Despite some blockchain-based HRM and employee management systems, none of the existing blockchain-based systems have addressed the problems of employee transfers. We propose a secure and transparent blockchain-based employee transfer (E-transfer) system to address this. The proposed system uses smart contracts for transfer management, recording outcomes on an unchangeable ledger. We conducted tests on Hyperledger Fabric for our proposed system. Our study aims to introduce an automated, auditable, and efficient transfer process. The findings of the study validate the effectiveness of the blockchain-based E-transfer system.

关键词： Electric potential Distributed ledger Smart contracts Force Focusing Organizations Fabrics Blockchains Recording Recruitment

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