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Review of recent designs, performance, and configurations for the pyramid solar still

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International Journal of Energy and Water Resources 2025年第2期9卷 1145-1176页

作者： Hussein, A. Kadhim Rashid, F. Lafta Abed, A.M. Sultan, H.S. Togun, H. Attia, M. El Hadi Manokar, A. Muthu Hamida, M. Bechir Ben Ali, B. Younis, O. Khutar, N. Murad Mechanical Engineering Department College of Engineering University of Babylon Babylon City Hilla Iraq Petroleum Engineering Department College of Engineering University of Kerbala Karbala Iraq Air Conditioning and Refrigeration Techniques Engineering Department Al-Mustaqbal University College Babylon 51001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbalaa 56001 Iraq Biomedical Engineering Department University of Thi-Qar Nassiriya 64001 Iraq Physics Department Faculty of Exact Sciences University of El Oued El Oued 3900 Algeria Department of Mechanical Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai 600048 India Department of Chemical Engineering College of Engineering Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Research Laboratory of Ionized Backgrounds and Reagents Studies (EMIR) Preparatory Institute for Engineering Studies of Monastir (IPEIM) University of Monastir Monastir City Tunisia Higher School of Sciences and Technology of Hammam Sousse Department of Physics University of Sousse Sousse Tunisia School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen 518055 China Department of Mechanical Engineering College of Engineering in Wadi Addwasir Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Mechanical Engineering Faculty of Engineering University of Khartoum Khartoum Sudan Department of Computer Technical Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq

Fresh water is essential for day-to-day survival. The primary issue facing undeveloped nations is the continuous scarcity of clean water, and drinking contaminated water to survive may harm one’s health. Due to population expansion, there is less fresh water available. Boring wells, rainwater, and water from rivers or lakes are among the potential water sources. For isolated, arid, and semiarid regions, solar distillation is appropriate. Where there is a severe drinking water deficit and intense solar radiation. With free energy, straightforward, affordable technology, and a clean atmosphere, solar still (SS) partly meets man’s demand for drinking water, especially in distant locations. This review focuses on pyramid solar stills (PSS), highlighting their potential to meet drinking water demands with free, clean energy and simple technology. This paper comprehensively reviews numerical, experimental, and combined (numerical and experimental), discussing various setups, parameters, and results. Key advancements, performance improvements, and future research directions are outlined, emphasizing the importance of innovative designs and optimization for enhanced efficiency and freshwater production. © The Author(s), under exclusive licence to Iranian Society of Environmentalists (IRSEN) and science and Research Branch, Islamic Azad University 2024.

关键词： Comprehensive review Pyramid solar still Remote regions Water desalination

Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

RIEMANNIAN GEOMETRY-BASED EEG APPROACHES: A LITERATURE REVIEW

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arXiv 2024年

作者： Tibermacine, Imad Eddine Russo, Samuele Tibermacine, Ahmed Rabehi, Abdelaziz Nail, Bachir Kadri, Kamel Napoli, Christian Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Department of Psychology Sapienza University of Rome Rome Italy Department of Computer Science University of Biskra Biskra Algeria Faculty of Science and Technology University of Djelfa Djelfa Algeria Polytechnic Military School Algiers Algeria Institute for Systems Analysis and Computer Science Italian National Research Council Italy Department of Computational Intelligence Czestochowa University of Technology Poland

The application of Riemannian geometry in the decoding of brain-computer interfaces (BCIs) has swiftly garnered attention because of its straightforwardness, precision, and resilience, along with its aptitude for transfer learning, which has been demonstrated through significant achievements in global BCI competitions. This paper presents a comprehensive review of recent advancements in the integration of deep learning with Riemannian geometry to enhance EEG signal decoding in BCIs. Our review updates the findings since the last major review in 2017, comparing modern approaches that utilize deep learning to improve the handling of non-Euclidean data structures inherent in EEG signals. We discuss how these approaches not only tackle the traditional challenges of noise sensitivity, non-stationarity, and lengthy calibration times but also introduce novel classification frameworks and signal processing techniques to reduce these limitations significantly. Furthermore, we identify current shortcomings and propose future research directions in manifold learning and riemannian-based classification, focusing on practical implementations and theoretical expansions, such as feature tracking on manifolds, multitask learning, feature extraction, and transfer learning. This review aims to bridge the gap between theoretical research and practical, real-world applications, making sophisticated mathematical approaches accessible and actionable for BCI enhancements. © 2024, CC BY.

关键词： Geometry

Domain-adaptive Person Re-identification without Cross-camera Paired Samples

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arXiv 2023年

作者： Li, Huafeng Mao, Yanmei Zhang, Yafei Qi, Guanqiu Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Information Systems Department State University of New York at Buffalo State BuffaloNY14222 United States

Existing person re-identification (re-ID) research mainly focuses on pedestrian identity matching across cameras in adjacent areas. However, in reality, it is inevitable to face the problem of pedestrian identity matching across long-distance scenes. The cross-camera pedestrian samples collected from long-distance scenes often have no positive samples. It is extremely challenging to use cross-camera negative samples to achieve cross-region pedestrian identity matching. Therefore, a novel domain-adaptive person re-ID method that focuses on cross-camera consistent discriminative feature learning under the supervision of unpaired samples is proposed. This method mainly includes category synergy co-promotion module (CSCM) and cross-camera consistent feature learning module (CCFLM). In CSCM, a task-specific feature recombination (FRT) mechanism is proposed. This mechanism first groups features according to their contributions to specific tasks. Then an interactive promotion learning (IPL) scheme between feature groups is developed and embedded in this mechanism to enhance feature discriminability. Since the control parameters of the specific task model are reduced after division by task, the generalization ability of the model is improved. In CCFLM, instance-level feature distribution alignment and cross-camera identity consistent learning methods are constructed. Therefore, the supervised model training is achieved under the style supervision of the target domain by exchanging styles between source-domain samples and target-domain samples, and the challenges caused by the lack of cross-camera paired samples are solved by utilizing cross-camera similar samples. In experiments, three challenging datasets are used as target domains, and the effectiveness of the proposed method is demonstrated through four experimental settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

An Optimal Path Planning Method with Assigned Endpoints in Grid Maps

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An Optimal Path Planning Method with Assigned Endpoints in G...

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiaoxiong Wang Zhuo Wang Zhikang Ge Zhihong Man Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Faculty of Science Engineering and Technology Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

In this paper, an optimal path planning method with assigned endpoints is developed for complete coverage in grid maps. It is shown that, the complete coverage path can be generated by merging the coverage paths of multiple subregions. Initially, several path planning templates are constructed to minimize repetition in regular regions. Subsequently, the grid map is decomposed into multiple subregions based on the locations of the assigned endpoints. Each region is then traversed with the corresponding template according to the parity of the region. Finally, all paths within the subregions are merged to form a comprehensive coverage path. It is demonstrated that the resulting path exhibits minimal repetition while covering the grid map completely with assigned endpoints. Simulation results are provided to validate the excellent performance of presented path planning method.

关键词： Industrial electronics Simulation Merging Path planning Computational complexity

OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering

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arXiv 2025年

作者： Ding, Guanhua Xia, Yuxuan Guan, Runwei Wu, Qinchen Huang, Tao Ding, Weiping Sun, Jinping Mao, Guoqiang School of Electronic Information Engineering Beihang University Beijing100191 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou511455 China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Research Laboratory of Smart Driving and Intelligent Transportation Systems Southeast University Nanjing210096 China

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

Melanoma Detection using a Random Forest Algorithm 10

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Melanoma Detection using a Random Forest Algorithm

10th E-Health and Bioengineering Conference, EHB 2022

作者： Damian, Felicia-Anisoara Moldovanu, Simona Moraru, Luminita The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (纸本)9781665485579

In recent years, statistical evidence has identified melanoma as a major cause of skin cancer deaths. In this study, we propose a skin analysis lesions based on colour features (C) and Hu (H) moments to differentiate benign (nevi) and malignant (melanoma) lesions and, a classification based on Random Forest algorithm. Colour features are found as follows. Pre-processing for hair removing using the Dull-razor method;image segmentation based on the optimal colour threshold set on each RGB colour channel (red, green, blue);23 colour features extraction using the minimum and maximum RGB intensity and Hu moments;Random Forest (RF) algorithm to accurately differentiate benign and malignant skin lesions. This study used three datasets: 7-Point, Med-Node, PH2. RF classifier performance in terms of accuracy was: 94% for the 7-point dataset with H5, H7, H6, H2, C08 selected as relevant features;98.1% for the Med-Node dataset and H7, H5, H6, H2, H1 ACC the relevant features;and, 96.5% for the PH2 dataset and H5, H6, H7, C03, H1 selected as relevant features, respectively. Shape features based on Hu moments are found to improve the classification performance of the skin lesions compared to the colour features. © 2022 IEEE.

关键词： Image segmentation

Transformer-based Single-Cell Language Model: A Survey

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arXiv 2024年

作者： Lan, Wei He, Guohang Liu, Mingyang Chen, Qingfeng Cao, Junyue Peng, Wei The Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronic and Information Guangxi University Nanning China The School of Computer and Electronic Information Guangxi University Nanning China The School of Computer Electronic and Information Guangxi University Nanning China The College of Life Science and Technology Guangxi University Nanning China The Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

The transformers have achieved significant accomplishments in the natural language processing as its outstanding parallel processing capabilities and highly flexible attention mechanism. In addition, increasing studies based on transformers have been proposed to model single-cell data. In this review, we attempt to systematically summarize the single-cell language models and applications based on transformers. First, we provide a detailed introduction about the structure and principles of transformers. Then, we review the single-cell language models and large language models for single-cell data analysis. Moreover, we explore the datasets and applications of single-cell language models in downstream tasks such as batch correction, cell clustering, cell type annotation, gene regulatory network inference and perturbation response. Further, we discuss the challenges of single-cell language models and provide promising research directions. We hope this review will serve as an up-to-date reference for researchers interested in the direction of single-cell language models. Copyright © 2024, The Authors. All rights reserved.

关键词： Cytology

Improved Classification of Benign and Malignant Breast Lesions Based on Contrast Manipulation and Gradient Map 10

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Improved Classification of Benign and Malignant Breast Lesio...

10th E-Health and Bioengineering Conference, EHB 2022

作者： Nastase, Iulia-Nela Anghelache Moldovanu, Simona Moraru, Luminita The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (纸本)9781665485579

This study proposes an approach to enhance the contrast breast ultrasound (BUS) images with the preservation of the finer details. The main idea is to use the relevant and most important features in the classification of BUS images as benign or malignant. Among the contrast manipulation techniques, both linear under- and overexposure methods, contrast limiting adaptive histogram equalization (CLAHE) method and homomorphic filtering are used. The gradient maps of original images are generated for further analysis. Also, the gradient maps of original images are improved by the local mean subtraction and divisive normalization. Eight structural features are extracted from each gradient map image type. The extracted features feed a Random Forest (RF) classifier to determine their relevance for BUS images classification. An accuracy of 97.12% has been obtained for improved gradient map images and 93.03% for gradient map images. © 2022 IEEE.

关键词： Image classification