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A hybrid flow shop scheduling optimization using puma optimizer: Addressing energy efficiency and workforce restrictions

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Alexandria engineering Journal 2025年 128卷 340-365页

作者： Sang-Woong Lee Jawad Tanveer Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Benyamin Abdollahzadeh Hamid Rokny Thantrira Porntaveetus Jan Lansky Mehdi Hosseinzadeh Pattern Recognition and Machine Learning Lab School of Computing Gachon University Seongnam 13120 Republic of Korea Department of Computer Science and Engineering Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Center of Excellence in Precision Medicine and Digital Health Department of Physiology Faculty of Dentistry Chulalongkorn University Bangkok 10330 Thailand Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic School of Computer Science Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

The Hybrid Flow Shop Scheduling Problem (HFSP) is a complex challenge in production and manufacturing, requiring efficient scheduling solutions that optimize productivity while considering real-world constraints. This study proposes an advanced optimization framework utilizing the Puma Optimizer, a metaheuristic algorithm enhanced with an adaptive control mechanism for intelligently selecting phases and operators. This feature makes it well-suited for discrete optimization problems such as HFSP. Our approach integrates heuristic decoding and adaptive local search, balancing exploration and exploitation to generate high-quality schedules. The proposed mathematical model optimizes makespan and total tardiness while energy consumption is incorporated as a constraint to enhance sustainability. Extensive computational experiments demonstrate the superior performance of the proposed algorithm compared to traditional methods, achieving shorter makespan and lower tardiness while ensuring workforce feasibility. These results highlight the Puma Optimizer’s adaptability and potential to set new benchmarks for HFSP, offering a scalable and effective solution for modern production systems.

关键词： Hybrid Flow Shop Scheduling Problem (HFSP) Puma optimizer Energy efficiency Worker constraints Metaheuristic optimization

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Optimal combination of photovoltaic systems and wind generators in the power network

Optimal combination of photovoltaic systems and wind generat...

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International Conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Maria Fotopoulou George Tsekouras Vassiliki Kontargyri Dimitrios Rakopoulos Centre for Research and Technology Hellas Chemical Process and Energy Resources Institute Athens Greece Department of Electrical and Electronics Engineering University of West Attica Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

The current energy transition has affected the design and operation of power networks, which need to incorporate Renewable Energy Sources (RES) to reduce their environmental footprint. More specifically, depending on the region, power networks need to meet certain target values related to RES penetration. In this context, Photovoltaic (PV) systems and Wind Generators (WG) seem to be two of the most widespread solutions. Nevertheless, their production is stochastic, which often leads to a mismatch with the demand resulting in either the need for storage systems or curtailments. The purpose of this research is to present an algorithm for the selection of the optimal combination of PV systems and WGs in power networks to meet the renewable energy targets and avoid or minimize the need for storage or curtailments. The core of the algorithm is a Nonlinear Programming (NLP) optimizer. The algorithm is applied on a power network in Slovenia and showcases the optimal combination of PVs and WGs to achieve 40% RES penetration (i.e., the European Union target for 2030), which is equal to 12.44 MW and 16.78 MW of installed power, respectively.

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2D/2D heterostructure Ni-Fe LDH/black phosphorus nanosheets with AuNP for noxious substance diphenylamine detection in food samples

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Food Chemistry 2024年 432卷 137295页

作者： Prasanna, Sanjay Ballur Lin, Yu-Chien Ramaraj, Sayee Kannan Dhawan, Udesh Liu, Xinke Tung, Ching-Wei Sakthivel, Rajalakshmi Chung, Ren-Jei Taipei10608 Taiwan PG& Research Department of Chemistry Thiagarajar College Tamilnadu Madurai 09 India Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore117583 Singapore Department of Materials Engineering Ming Chi University of Technology New Taipei City Taiwan Taipei10608 Taiwan

In this study, gold nanoparticles decorated on nickel–iron layered double hydroxide with black phosphorus nanosheets (AuNP/Ni-Fe LDH/BPNSs) composite were prepared using a stirring method. Analyte tracing is required for developing viable sensors. The AuNP/Ni-Fe LDH/BPNSs composite exhibited a large specific surface area, high conductivity, high electrocatalytic activity, and rapid electron transfer. These properties play a vital role in monitoring diphenylamine (DPA) in food samples. The formation of the AuNP/Ni-Fe LDH/BPNSs composite was confirmed using various structural and morphological characterization techniques. The electroanalytical character of the AuNP/Ni-Fe LDH/BPNSs composite was evaluated using voltammetry. Interestingly, the AuNP/Ni-Fe LDH/BPNSs showed a wide linear range of 0.0125–1003.82 μM and a detection limit of 4.63 nM with a sensitivity of 0.399 µA µM−1 cm−2. The constructed sensor shows considerable selectivity, stability, repeatability, and reproducibility, and the practicability of DPA was monitored in the apples, sweet tomatoes, pears, and grapes with satisfactory recoveries. © 2023 Elsevier Ltd

关键词： Electrooxidation

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Automated End-To-End AI/ML Lifecycles for Radio Management in 6G Networks

Automated End-To-End AI/ML Lifecycles for Radio Management i...

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IEEE Future Networks World Forum (FNWF)

作者： Haya Al Kassir Anastasios Giannopoulos Sotirios Spantideas Panagiotis Trakadas George Xylouris Michail-Alexandros Kourtis Emmanouil Pateromichelakis Konstantinos Samdanis Sławomir Kukliński Tao Chen Zaharias D. Zaharis Four Dot Infinity Athens Greece National Center of Scientific Research "Demokritos" Athens Greece Lenovo Germany Stuttgart Germany Warsaw University of Technology Warsaw Poland VTT Technical Research Centre of Finland Espoo Finland School of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

Intelligent radio resource management is expected to play a vital role in addressing the strict user demands, possessing new challenges in the era of 6G services. In this paper, we provide an extensive study of artificial intelligence and machine learning (AI/ML) lifecycles, within the open radio access network (O-RAN) framework with particular emphasis on radio resource management advancement. More specifically, considering a multi-layered 6G network system, the AI/ML Cross-Layer Platform (AI-CLatform) is introduced to leverage AI/ML lifecycles devoted for O-RAN operation. With the near real-time RAN intelligent controller (Near-Rt RIC) devoted for radio intelligence, special emphasis is given on the development, deployment, optimization, and continuous monitoring of ML models within O-RAN, whereas the interactions between O-RAN and AI-CLatform are justified. To concretely illustrate the proposed end-to-end AI/ML sequential process, we present a proof-of-concept (PoC) practical scenario focusing on intelligent beamforming optimization for proactively managing the interference using recurrent neural networks (RNNs). The quantitative simulation findings prove the potential of the proposed AI/ML framework in enhancing critical 6G network functions within the O-RAN paradigm.

关键词： 6G mobile communication Recurrent neural networks Array signal processing Focusing Open RAN Predictive models Real-time systems Resource management Optimization Network systems

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ARGai: AI Fusion Approaches for Classification of S. aureus Strains and Identification of Antibiotic Resistant Genes

ARGai: AI Fusion Approaches for Classification of S. aureus ...

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Artificial Intelligence and Emerging technology (Global AI Summit), International Conference on

作者： Debasish Swapnesh Kumar Nayak Archana Dash Sweta Padma Routray Santanu Sahoo Tripti Swarnkar Department of Computer Science and Engineering FET-ITER Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Department of Computer Science and Engineering SOET Centurion University of Technology and Management Bhubaneswar India Centre of Biotechnology Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India Department of ECE Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India Department of Computer Application National Institute of Technology Raipur India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Staphylococcus aureus (S. aureus) causes bone and joint infections, pneumonia, and bloodstream infections, the most common and fatal of these illnesses. Rapid identification of resistant strains and the antibiotic resistance genes (ARGs) linked to them can enhance the diagnostic process for various infectious illnesses. We introduce a new clinical support system that uses a hybrid multi-stage significant gene identification technique called MRMR-LASSO to select resistant and significant genes from next-generation sequencing (NGS) datasets. It is necessary to identify the S. aureus resistance strain from among thousands of relevant genes in NGS datasets, which are typically complex and comprise many genes with few samples. The curse of dimensionality makes it hard to pick informative genes from many genes. Optimization techniques are employed to resolve the gene selection issue. To select important genes for our ARGai model, we employed a filter in conjunction with an embedding approach. With its combination of machine learning (ML) and deep learning (DL) methods, ARGai can efficiently and accurately identify the important genes in S. aureus with a 96% success rate. Examining the biological relevance of chosen gene activity guarantees that ARGai's classification performance on susceptible and resistant strains is outstanding. Several cutting-edge AI models used for gene selection and strain classification are compared to our ARGai model. By shortening the screening period, this proposed ARGai may aid in subsequent phases of illness treatment, such as disease diagnosis and medication design.

关键词： Sequential analysis Pneumonia Biological system modeling Antibiotics Medical diagnosis Next generation networking Optimization Medical diagnostic imaging Strain Immune system

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Synergistic effect of photothermal and magnetic hyperthermia for in situ activation of Fenton reaction in tumor microenvironment for chemodynamic therapy

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Biomaterials Advances 2024年 157卷 213724页

作者： Thirumurugan, Senthilkumar Dash, Pranjyan Lin, Yu-Chien Sakthivel, Rajalakshmi Sun, Ying-Sui Lin, Ching-Po Wang, An-Ni Liu, Xinke Dhawan, Udesh Tung, Ching-Wei Chung, Ren-Jei Taipei106344 Taiwan School of Dental Technology College of Oral Medicine Taipei Medical University Taipei110301 Taiwan Institute of Neuroscience National Yang Ming Chiao Tung University Taipei112304 Taiwan Scrona AG Zürich8045 Switzerland College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow G116EW Glasgow United Kingdom Department of Materials Engineering Ming Chi University of Technology New Taipei City243303 Taiwan Taipei106344 Taiwan

Traditional cancer treatments are ineffective and cause severe adverse effects. Thus, the development of chemodynamic therapy (CDT) has the potential for in situ catalysis of endogenous molecules into highly toxic species, which would then effectively destroy cancer cells. However, the shortage of high-performance nanomaterials hinders the broad clinical application of this approach. In present study, an effective therapeutic platform was developed using a simple hydrothermal method for the in-situ activation of the Fenton reaction within the tumor microenvironment (TME) to generate substantial quantities of •OH and ultimately destroy cancer cells, which could be further synergistically increased by photothermal therapy (PHT) and magnetic hyperthermia (MHT) aided by FeMoO4 nanorods (NRs). The produced FeMoO4 NRs were used as MHT/PHT and Fenton catalysts. The photothermal conversion efficiency of the FeMoO4 NRs was 31.75 %. In vitro and \ experiments demonstrated that the synergistic combination of MHT/PHT/CDT notably improved anticancer efficacy. This work reveals the significant efficacy of CDT aided by both photothermal and magnetic hyperthermia and offers a feasible strategy for the use of iron-based nanoparticles in the field of biomedical applications. © 2023

关键词： Nanorods

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Parkinson's Disease Classification from Scanned Images of Spiral Drawings

Parkinson's Disease Classification from Scanned Images of Sp...

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Artificial Intelligence for Medicine, Health and Care (AIMHC), IEEE International Conference on

作者： Anusid Wachiracharownong Panyawut Sri-iesaranusorn Decho Surangsrirat Pattara Leelaprute Pattamon Panyakaew Roongroj Bhidayasiri Department of Computer Engineering Faculty of Engineering Kasetsart University Bangkok Thailand National Science and Technology Development Agency Assistive Technology and Medical Devices Research Center Pathum Thani Thailand Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders Thai Red Cross Society Chulalongkorn University and King Chulalongkorn Memorial Hospital Bangkok Thailand

Early detection of Parkinson's disease is crucial to minimize its impact on patients. Tremor is a prevalent symptom, yet traditional diagnostic methods require physical examinations. These can pose challenges for elderly patients, particularly as the disease progresses. To address this issue, we proposed a method that analyzes Archimedean spiral drawings from a scanned image. It enables users to perform screening tests at home without the need for any special equipment, making it accessible and user-friendly for people of all ages, especially seniors. We conducted experiments on 88 individuals, including 35 healthy control participants and 53 PD patients. To achieve high accuracy, we applied data augmentation techniques to the training data and tested multiple pre-trained convolutional neural network models. Our results indicate that InceptionV3achieved a respectable performance with 82% accuracy and an F1 score of 0.81 ± 0.02. Our proposed method offers a cost-effective and convenient alternative for early PD detection and screening. It enables at-home patient screening without specialized equipment or visiting a medical facility. This is especially beneficial for individuals in rural or remote areas who may have limited access to medical facilities.

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Supertransport of excitons in atomically thin organic semiconductors at the 2D quantum limit

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Light(Science & Applications) 2020年第1期9卷 907-919页

作者： Ankur Sharma Linglong Zhang Jonathan O.Tollerud Miheng Dong Yi Zhu Robert Halbich Tobias Vogl Kun Liang Hieu T.Nguyen Fan Wang Shilpa Sanwlani Stuart K.Earl Daniel Macdonald Ping Koy Lam Jeffrey A.Davis Yuerui Lu Research School of Electrical Energy and Materials EngineeringCollege of Engineering and Computer ScienceThe Australian National UniversityCanberraACT 2601Australia Optical Sciences Centre Swinburne University of TechnologyHawthornVIC 3122Australia ARC Centre of Excellence for Future Low-Energy Electronics Technology Australia Centre for Quantum Computation and Communication Technology Department of Quantum ScienceResearch School of Physics and EngineeringThe Australian National UniversityActonACT 2601Australia School of Mechatronical Engineering Beijing Institute of TechnologyBeijing 100081China Institute for Biomedical Materials and Devices(IBMD) Faculty of ScienceUniversity of Technology SydneySydneyNSW 2007Australia

Long-range and fast transport of coherent excitons is important for the development of high-speed excitonic circuits and quantum computing ***,most of these coherent excitons have only been observed in some low-dimensional semiconductors when coupled with cavities,as there are large inhomogeneous broadening and dephasing effects on the transport of excitons in their native states in ***,by confining coherent excitons at the 2D quantum limit,we first observed molecular aggregation-enabled‘supertransport’of excitons in atomically thin two-dimensional(2D)organic semiconductors between coherent states,with a measured high effective exciton diffusion coefficient of ~346.9 cm^(2)/s at room *** value is one to several orders of magnitude higher than the values reported for other organic molecular aggregates and low-dimensional inorganic *** coupling to any optical cavities,the monolayer pentacene sample,a very clean 2D quantum system(~1.2 nm thick)with high crystallinity(J-type aggregation)and minimal interfacial states,showed superradiant emission from Frenkel excitons,which was experimentally confirmed by the temperature-dependent photoluminescence(PL)emission,highly enhanced radiative decay rate,significantly narrowed PL peak width and strongly directional in-plane *** coherence in monolayer pentacene samples was observed to be delocalised over~135 molecules,which is significantly larger than the values(a few molecules)observed for other organic thin *** addition,the supertransport of excitons in monolayer pentacene samples showed highly anisotropic *** results pave the way for the development of future high-speed excitonic circuits,fast OLEDs,and other optoelectronic devices.

关键词： transport quantum coherent

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PwLMμ-TPE: A Visual-Usability and Privacy-Enhanced Thumbnail Preserving Encryption Scheme of Cloud Computing for Consumers

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Chowdhury, Keya Deb, Subhrajyoti Kar, Nirmalya Sarkar, Joy Lal Alkhayyat, Ahmed Hussein Nkenyereye, Lewis Department of Computer Science and Engineering ICFAI University Tripura India Department of CSE ICFAI University Tripura India Department of CSE National Institute of Technology Agartala India Department of CSE Techno College of Engineering Tripura India scientific research centre of the Islamic university The Islamic University Najaf Iraq Department of Computer and Information Security Sejong University South Korea

The speedy proliferation of cloud storage choices has prompted consumers to choose the cloud as a preferred platform for storing their collections of images. However, this trend also invokes fundamental security issues regarding image privacy and data breach scenarios. To address these issues, consumers have recently used miscellaneous traditional image encryption techniques, primarily concentrated on preventing unauthorized access to cloud-stored images to defend the privacy of the image data. Moreover, these methods prioritize privacy preservation, they often neglect the importance of maintaining the usability of the encrypted images;hence, a Thumbnail-preserving encryption (TPE) was newly raised to preserve the thumbnail unchanged post-encryption, thereby improving the prevailing usability of the stored image. Hence, legitimate consumers can conveniently preview and control the cipher image without decryption. PwLMμ-TPE scheme greatly decreases encryption durations by approximately 17 times compared to existing methods, achieving an expansion ratio of around 2.1 for encrypted images and 1 for decrypted images, while showing SSIM and PSNR values close to 0.9 and infinity, respectively, along with an NPCR value of nearly 99%. The scheme is favourably adequate to resist cryptographic attacks and ensures usability and privacy, making it particularly suitable for cloud-based applications. IEEE

关键词： Cryptography

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