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Drying and Extraction of Moringa Oleifera and Its Application in Wastewater Treatment 15

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Drying and Extraction of Moringa Oleifera and Its Applicatio...

15th International Engineering and Computing Research Conference, EURECA 2021

作者： Ibrahim, M. Ismail, N. Chua, B.L. Adnan, A.S.M. School of Computer Science and Engineering Faculty of Innovation and Technology Taylor's University Lakeside Campus Jalan Taylor's Selangor Darul Ehsan Subang Jaya47500 Malaysia Postharvest Handling Programme Paddy and Rice Research Centre MARDI Headquarters Persiaran MARDI-UPM Selangor Darul Ehsan Serdang43400 Malaysia

Conventional coagulants such as aluminum sulfate and ferric sulphate have proved to be harmful to human health as it may cause neurological diseases like Alzheimer and percentile dementia. These health concerns have led to the use of natural coagulants which are environmental friendly and not harmful to human health. In this study, Moringa Oleifera seeds are selected to study the preparation of the natural coagulant and further use it in dye wastewater treatment. The two important steps used in preparing the natural coagulants are drying of the raw materials and extraction of the protein content. Moringa Oleifera seeds were oven dried at 40°C, 50°C and 60°C and the drying data was recorded. The recorded data was then fitted into five thin layer kinetic models to obtain the most suitable drying temperature and its corresponding drying time. Using Microsoft Excel Solver 2011, values of (R2), (RMSE) and chi-square (X2 ) were calculated. Based on the highest value of (R2 ) and lowest value of (RMSE), Page kinetic model was selected. Moreover, for the selected Page kinetic model, values of (R2) and (X2) were also compared for all three temperatures and the temperature with the highest (R2) value and lowest chi-square value (X2) was selected as the most optimum drying temperature. The most optimum drying temperature for drying Moringa Oleifera seeds was obtained as 50°C as the seeds dried at this temperature holds the maximum protein content concentration which enhances its coagulation properties. Furthermore, the corresponding drying time of about 6.5 hours for seeds dried at 50°C was also taken as the most optimum drying time. In the next step, Moringa Oleifera seeds which have been dried at 50°C for 6.5 hours were used for protein extraction. Solid to solvent ratios of 0.5:100, 1:100, 2:100 and 5: 100 (g of dried Moringa oleifera seeds: ml of distilled water) were used to extract the protein by using a domestic blender for 2 minutes. Using the Bradford method, the abso

关键词： Sulfur compounds

Evaluation of competencies for academic internship students 11

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Evaluation of competencies for academic internship students

11th International Conference on Virtual Campus, JICV 2021

作者： Tinoco-Giraldo, Harold María Torecilla Sánchez, Eva García-Peñalvo, Francisco J. Universidad de Salamanca GRIAL Research Group Doctoral Programme Education in the Knowledge Society Salamanca Spain Universidad de Salamanca GRIAL Research Group Research Institute for Educational Sciences Salamanca Spain Research Institute for Educational Sciences Universidad de Salamanca GRIAL Research Group Computer Science Department Salamanca Spain

ISBN: (纸本)9781665437035

The rules and regulations on academic internships require all trainees to be 'competent' and 'qualified', but there is little direction on how institutions can prove that their workers and internship personal are knowledgeable and qualified. Competencies necessity to be correctly assessed, tested and verify, to fulfill the regulations of academic internship experiences, so that competencies continue to be assessed by a 'competency standard', which specifies all the skills, talents, knowledge, abilities and experience required for each competency. This paper provides a competency assessments procedure used for academic internship practices. © 2021 IEEE.

关键词： Regulation

Critically examining the Domain Generalizability of Facial Expression Recognition models

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arXiv 2021年

作者： Suresh, Varsha Yeo, Gerard Ong, Desmond C. Department of Computer Science National University of Singapore Singapore Institute of Data Science Integrative Sciences and Engineering Programme National University of Singapore Singapore Department of Psychology The University of Texas at Austin United States

Facial Expression Recognition is a commercially-important application, but one under-appreciated limitation is that such applications require making predictions on out-of-sample distributions, where target images have different properties from the images the model was trained on. How well—or how badly—do facial expression recognition models do on unseen target domains? We provide a systematic and critical evaluation of transfer learning—specifically, domain generalization—in facial expression recognition. Using a state-of-the-art model with twelve datasets (six collected in-lab and six "in-the-wild"), we conduct extensive round-robin-style experiments to evaluate classification accuracies when given new data from an unseen dataset. We also perform multi-source experiments to examine a model’s ability to generalize from multiple source datasets, including (i) within-setting (e.g., lab to lab), (ii) cross-setting (e.g., in-the-wild to lab), and (iii) leave-one-out settings. Finally, we compare our results with three commercially-available software. We find sobering results: the accuracy of single- and multi-source domain generalization is only modest. Even for the best-performing multi-source settings, we observe average classification accuracies of 65.6% (range: 34.6%-88.6%;chance: 14.3%), corresponding to an average drop of 10.8 percentage points from the within-corpus classification performance (mean: 76.4%). We discuss the need for regular, systematic investigations into the generalizability of affective computing models and applications. © 2021, CC BY.

关键词： Classification (of information)

Hybrid Global Flexible Power Point Tracking Algorithm for Photovoltaic Systems

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Hybrid Global Flexible Power Point Tracking Algorithm for Ph...

European Conference on Power Electronics and Applications

作者： Anusha Kumaresan Hossein Dehghani Tafti Glen G. Farivar Josep Pou Kai Strunz Dusit Niyato Energy Research Institute at NTU Interdisciplinary Graduate Programme Nanyang Technological University Singapore Engineering Institute of Technology (EIT) Australia Department of Electrical and Electronic Engineering The University of Melbourne Victoria Australia School of Electrical and Electronic Engineering Nanyang Technological University Singapore Chair for Sustainable Electric Networks and Sources of Energy Technische Universität Berlin Germany School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350364446

ISBN: (纸本)9798350364453

Frequency support in grid-connected photovoltaic (PV) systems is achieved by employing flexible power point tracking (FPPT) algorithms. Subsequently, according to the grid frequency requirements, the PV power is curtailed to a power reference, unlike the traditional method of operating the PV at the maximum power point. In practical cases, the PV arrays are prone to partial shading, and hence, there is a requirement to formulate the FPPT algorithms that operates effectively during such cases. Moreover, fast transient response is a necessity, which implies that the algorithms proposed in this paper is desirable. This paper proposes a hybrid global FPPT (GFPPT) algorithm based on secant and Newton’s method, to shift the PV operating point to the required power reference. The Newton’s method offers quadratic convergence rate and the secant method guarantees convergence. Hence, a hybrid algorithm consisting of these two methods will provide both fast and guaranteed convergence. Validation through simulation is presented in this paper and the proposed method is compared with the recently presented perturb and observe (P&O) GFPPT algorithm in the literature.

关键词： Photovoltaic systems Transient response Europe Voltage Hybrid power systems Convergence

Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization

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arXiv 2023年

作者： Yang, Siyuan Liu, Jun Lu, Shijian Hwa, Er Meng Hu, Yongjian Kot, Alex C. The Rapid-Rich Object Search Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The School of Computer Science & Engineering Nanyang Technological University Singapore The School of Electrical and Electronic Engineering Nanyang Technological University Singapore The South China University of Technology Guangzhou China

3D Skeleton-based human action recognition has attracted increasing attention in recent years. Most of the existing work focuses on supervised learning which requires a large number of labeled action sequences that are often expensive and time-consuming to annotate. In this paper, we address self-supervised 3D action representation learning for skeleton-based action recognition. We investigate self-supervised representation learning and design a novel skeleton cloud colorization technique that is capable of learning spatial and temporal skeleton representations from unlabeled skeleton sequence data. We represent a skeleton action sequence as a 3D skeleton cloud and colorize each point in the cloud according to its temporal and spatial orders in the original (unannotated) skeleton sequence. Leveraging the colorized skeleton point cloud, we design an auto-encoder framework that can learn spatial-temporal features from the artificial color labels of skeleton joints effectively. Specifically, we design a two-steam pretraining network that leverages fine-grained and coarse-grained colorization to learn multi-scale spatial-temporal features. In addition, we design a Masked Skeleton Cloud Repainting task that can pretrain the designed auto-encoder framework to learn informative representations. We evaluate our skeleton cloud colorization approach with linear classifiers trained under different configurations, including unsupervised, semi-supervised, fully-supervised, and transfer learning settings. Extensive experiments on NTU RGB+D, NTU RGB+D 120, PKU-MMD, NW-UCLA, and UWA3D datasets show that the proposed method outperforms existing unsupervised and semi-supervised 3D action recognition methods by large margins and achieves competitive performance in supervised 3D action recognition as well. Copyright © 2023, The Authors. All rights reserved.

关键词： Musculoskeletal system

NeuroHeed: Neuro-Steered Speaker Extraction using EEG Signals

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arXiv 2023年

作者： Pan, Zexu Borsdorf, Marvin Cai, Siqi Schultz, Tanja Li, Haizhou The Integrative Sciences and Engineering Programme the Institute of Data Science National University of Singapore 119077 Singapore The Machine Listening Lab the University of Bremen Bremen28359 Germany The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The Cognitive System Lab the University of Bremen Bremen28359 Germany The School of Data Science The Chinese University of Hong Kong Shenzhen518172 China The Machine Listening Lab University of Bremen Bremen28359 Germany

Humans possess the remarkable ability to selectively attend to a single speaker amidst competing voices and background noise, known as selective auditory attention. Recent studies in auditory neuroscience indicate a strong correlation between the attended speech signal and the corresponding brain’s elicited neuronal activities, which the latter can be measured using affordable and non-intrusive electroencephalography (EEG) devices. In this study, we present NeuroHeed, a speaker extraction model that leverages EEG signals to establish a neuronal attractor which is temporally associated with the speech stimulus, facilitating the extraction of the attended speech signal in a cocktail party scenario. We propose both an offline and an online NeuroHeed, with the latter designed for real-time inference. In the online NeuroHeed, we additionally propose an autoregressive speaker encoder, which accumulates past extracted speech signals for self-enrollment of the attended speaker information into an auditory attractor, that retains the attentional momentum over time. Online NeuroHeed extracts the current window of the speech signals with guidance from both attractors. Experimental results demonstrate that NeuroHeed effectively extracts brain-attended speech signals, achieving high signal quality, excellent perceptual quality, and intelligibility in a two-speaker scenario. Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

Hierarchical quantum embedding by machine learning for large molecular assemblies

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arXiv 2025年

作者： Bensberg, Moritz Eckhoff, Marco Husistein, Raphael T. Teynor, Matthew S. Sora, Valentina Bro-Jørgensen, William Thomasen, F. Emil Krogh, Anders Lindorff-Larsen, Kresten Solomon, Gemma C. Weymuth, Thomas Reiher, Markus ETH Zurich Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 2 Zurich8093 Switzerland University of Copenhagen Department of Chemistry and Nano-Science Center Universitetsparken 5 Copenhagen ØDK-2100 Denmark University of Copenhagen Niels Bohr Institute NNF Quantum Computing Programme Blegdamsvej 17 Copenhagen ØDK-2100 Denmark University of Copenhagen Department of Computer Science Universitetsparken 1 Copenhagen ØDK-2100 Denmark University of Copenhagen Department of Biology Linderstrøm-Lang Centre for Protein Science Ole Maaløes Vej 5 Copenhagen NDK-2200 Denmark University of Copenhagen Center for Health Data Science Department of Public Health Øster Farimagsgade 5 Copenhagen ØDK-1353 Denmark

We present a quantum-in-quantum embedding strategy coupled to machine learning potentials to improve on the accuracy of quantum-classical hybrid models for the description of large molecules. In such hybrid models, relevant structural regions (such as those around reaction centers or pockets for binding of host molecules) can be described by a quantum model that is then embedded into a classical molecular-mechanics environment. However, this quantum region may become so large that only approximate electronic structure models are applicable. To then restore accuracy in the quantum description, we here introduce the concept of quantum cores within the quantum region that are amenable to accurate electronic structure models due to their limited size. Huzinaga-type projection-based embedding, for example, can deliver accurate electronic energies obtained with advanced electronic structure methods. The resulting total electronic energies are then fed into a transfer learning approach that efficiently exploits the higher-accuracy data to improve on a machine learning potential obtained for the original quantum-classical hybrid approach. We explore the potential of this approach in the context of a well-studied protein-ligand complex for which we calculate the free energy of binding using alchemical free energy and non-equilibrium switching simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecules

Optimization of power transmission line location at tropical forest area in avoiding endangered tree species

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Arabian Journal of Geosciences 2022年第9期15卷 1-14页

作者： Abd Latif, Zulkiflee Ruslan, Sofiya Zulaikha Mohd Zaki, Nurul Ain Suratman, Mohd Nazip Omar, Hamdan Razak, Tajul Rosli Mohd Fauzi, Shukor Sanim GM Gining, Ray Adderley Institute for Biodiversity & Sustainable Development (IBSD) Universiti Teknologi MARA Shah Alam Malaysia Centre of Studies for Surveying Science and Geomatics Faculty of Architecture Planning and Surveying Universiti Teknologi MARA Shah Alam Malaysia Centre of Studies for Surveying Science and Geomatics Faculty of Architecture Planning and Surveying Universiti Teknologi MARA Arau Malaysia Geoinformation Programme Division of Forestry & Environment Forest Research Institute Malaysia (FRIM) Kepong Malaysia Faculty of Computer and Mathematical Science Universiti Teknologi MARA Arau Malaysia

Due to social defiance, there was a controversial issue regarding the development of new high-voltage overhead power lines (HVOPL) in electricity companies. In developing the infrastructure of new power infrastructure design, the psychological aspects of the country culture need to be considered and accept its advancement as part of important components in the physical environment of the community. Species of the tropical tree such as Aquilaria malaccensis and Dipterocarpus concavus was listed as Critically Endangered by International Union for Conservation of Nature (IUCN) Red List. Therefore, this paper is to overcome the issues of transmission line siting to avoid endangered tree species. Due to the rising demand for power supply, site exploration and automated technology development have become necessary. One of the suitable sites to set up the transmission line is in the tropical forest area, resulting in forest clearing for transmission Right-Of-Way (ROW) that threatens endangered tree species. This study aims to propose the optimization power transmission line sitting using geospatial data at the tropical forest in avoiding the endangered tree species. Previously, the best route for transmission line siting in a particular location was determined based on expert opinion and drawn manually on paper. Geographic Information System (GIS) technologies with the optimize least-cost path (LCP) siting by considering the related multi-criteria features including endangered tree species, slope, average wind speed, and human mask was introduced. The transmission line siting optimization using the reclassification algorithm to weigh the relevant multi-criteria and LCP tools carried out data processing on the Model Builder platform. Finally, mapping suitable power transmission line siting development using geospatial data in avoided endangered tree species in tropical forest areas. The output will be the guideline or reference for any utility company for transmission line

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Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays

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Physical Review X 2025年第2期15卷 021034-021034页

作者： Anantha S. Rao Donovan Buterakos Barnaby van Straaten Valentin John Cécile X. Yu Stefan D. Oosterhout Lucas Stehouwer Giordano Scappucci Menno Veldhorst Francesco Borsoi Justyna P. Zwolak Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA National Institute of Standards and Technology Gaithersburg Maryland 20899 USA QuTech and Kavli Institute of Nanoscience Delft University of Technology P.O. Box 5046 2600 GA Delft The Netherlands QuTech and Netherlands Organisation for Applied Scientific Research (TNO) Delft The Netherlands Present address: NNF Quantum Computing Programme Niels Bohr Institute University of Copenhagen Blegdamsvej 17 2100 Copenhagen Denmark.

Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capacitive crosstalk between gates hinders independent tuning of chemical potentials and interdot couplings. While virtual gates offer a practical solution, determining all the required cross-capacitance matrices accurately and efficiently in large quantum dot registers is an open challenge. Here, we establish a modular automated virtualization system (MAViS)—a general and modular framework for autonomously constructing a complete stack of multilayer virtual gates in real time. Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems.

关键词： Semiconductor quantum dots