scholarly journals Pigment analysis based on a line-scanning fluorescence hyperspectral imaging microscope combined with multivariate curve resolution

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254864
Author(s):  
Lijin Lian ◽  
Xuejuan Hu ◽  
Zhenhong Huang ◽  
Liang Hu ◽  
Lu Xu
Keyword(s):  
Spatial Information ◽  
Region Of Interest ◽  
Cost Effective ◽  
Optimization Method ◽  
Multivariate Curve Resolution ◽  
Pigment Analysis ◽  
Curve Resolution ◽  
Reconstruction Image ◽  
Effective System ◽  
Harmful Microalgae

A rapid and cost-effective system is vital for the detection of harmful algae that causes environmental problems in terms of water quality. The approach for algae detection was to capture images based on hyperspectral fluorescence imaging microscope by detecting specific fluorescence signatures. With the high degree of overlapping spectra of algae, the distribution of pigment in the region of interest was unknown according to a previous report. We propose an optimization method of multivariate curve resolution (MCR) to improve the performance of pigment analysis. The reconstruction image described location and concentration of the microalgae pigments. This result indicated the cyanobacterial pigment distribution and mapped the relative pigment content. In conclusion, with the advantage of acquiring two-dimensional images across a range of spectra, HSI conjoining spectral features with spatial information efficiently estimated specific features of harmful microalgae in MCR models.

scholarly journals Eco-friendly multivariate curve resolution-alternating least squares and chromatographic quantifications of some veterinary drug residues in pharmaceutical industrial wastewater

RSC Advances ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 2935-2946
Author(s):  
Osama I. Abdel Sattar ◽  
Hamed H. M. Abuseada ◽  
Mohamed S. Emara ◽  
Mahmoud Rabee
Keyword(s):  
Quantitative Analysis ◽  
Analytical Methods ◽  
Industrial Wastewater ◽  
Cost Effective ◽  
Multivariate Curve Resolution ◽  
Veterinary Drug ◽  
Veterinary Drug Residues ◽  
Drug Residues ◽  
Curve Resolution ◽  
Wastewater Samples

Three eco-friendly and cost-effective analytical methods were developed and optimized for quantitative analysis of some veterinary drug residues in production wastewater samples.

scholarly journals Edge-Preserving Image Smoothing Constraint in Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) of Hyperspectral Data

2017 ◽  
Vol 72 (3) ◽  
pp. 420-431 ◽  
Author(s):  
Siewert Hugelier ◽  
Raffaele Vitale ◽  
Cyril Ruckebusch
Keyword(s):  
Least Squares ◽  
Spatial Information ◽  
Hyperspectral Images ◽  
Alternating Least Squares ◽  
Multivariate Curve Resolution ◽  
Good Resolution ◽  
Spatial Constraint ◽  
Edge Preserving ◽  
Curve Resolution ◽  
Spectral Overlap

This article explores smoothing with edge-preserving properties as a spatial constraint for the resolution of hyperspectral images with multivariate curve resolution–alternating least squares (MCR-ALS). For each constrained component image (distribution map), irrelevant spatial details and noise are smoothed applying an L1- or L0-norm penalized least squares regression, highlighting in this way big changes in intensity of adjacent pixels. The feasibility of the constraint is demonstrated on three different case studies, in which the objects under investigation are spatially clearly defined, but have significant spectral overlap. This spectral overlap is detrimental for obtaining a good resolution and additional spatial information should be provided. The final results show that the spatial constraint enables better image (map) abstraction, artifact removal, and better interpretation of the results obtained, compared to a classical MCR-ALS analysis of hyperspectral images.

Extended Routine Follow-up of Primary Uncomplicated Hip and Knee Arthroplasty is not Necessary

2019 ◽  
Vol 13 (1) ◽  
pp. 266-271
Author(s):  
Georgina Kakra Wartemberg ◽  
Thomas Goff ◽  
Simon Jones ◽  
James Newman
Keyword(s):  
Aseptic Loosening ◽  
Revision Surgery ◽  
Revision Arthroplasty ◽  
Cost Effective ◽  
Face To Face ◽  
Primary Surgery ◽  
Effective System ◽  
Knee Revision Surgery ◽  
Knee Revision

Aims: To create a more effective system to identify patients in need of revision surgery. Background: There are over 160,000 total hip and knee replacements performed per year in England and Wales. Currently, most trusts review patients for up to 10 years or more. When we consider the cost of prolonged reviews, we cannot justify the expenditure within a limited budget. Study Design & Methods: We reviewed all patients' notes that underwent primary hip and knee revision surgery at our institution, noting age, gender, symptoms at presentation, referral source, details of the surgery, reason for revision and follow up history from primary surgery. Results: There were 145 revision arthroplasties (60 THR and 85 TKR) that met our inclusion criteria. Within the hip arthroplasty group, indications for revision included aseptic loosening (37), dislocation (10), and infection (3), periprosthetic fracture, acetabular liner wear and implant failure. All thirty-seven patients with aseptic loosening presented with pain. Twenty-five were referred from general practice with new symptoms. The remaining were clinic follow-ups. The most common reason for knee revision was aseptic loosening (37), followed by infection (21) and then progressive osteoarthritis (8). Most were referred from GP as a new referral or were clinic follow-ups. All patients were symptomatic. Conclusion: All the patients that underwent revision arthroplasty were symptomatic. Rather than yearly follow up, we recommend a cost-effective system. We are implementing a 'non face-to-face' system. Patients would be directly sent a questionnaire and x-ray form. The radiographs and forms will be reviewed by an experienced arthroplasty surgeon. The concerning cases will be seen urgently in a face-to-face clinic.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

Weighting hyperspectral image data for improved multivariate curve resolution results

2008 ◽  
Vol 22 (9) ◽  
pp. 482-490 ◽  
Author(s):  
Howland D. T. Jones ◽  
David M. Haaland ◽  
Michael B. Sinclair ◽  
David K. Melgaard ◽  
Mark H. Van Benthem ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Image Data ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Hyperspectral Image Data

scholarly journals Compliance as a Cost-Effective System of Interaction Between Business and Government

2021 ◽  
Author(s):  
Nikolay I. Dorogov ◽  
Ivan A. Kapitonov ◽  
Nazygul T. Batyrova
Keyword(s):  
Cost Effective ◽  
Effective System

scholarly journals Economic Development Based on a Mathematical Model: An Optimal Solution Method for the Fuel Supply of International Road Transport Activity

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2963
Author(s):  
Melinda Timea Fülöp ◽  
Miklós Gubán ◽  
György Kovács ◽  
Mihály Avornicului
Keyword(s):  
Decision Making ◽  
Ad Hoc ◽  
Market Competition ◽  
Optimal Solution ◽  
Cost Savings ◽  
Cost Effective ◽  
Optimization Method ◽  
Optimal Decision ◽  
Environmental Damage ◽  
Fuel Cost

Due to globalization and increased market competition, forwarding companies must focus on the optimization of their international transport activities and on cost reduction. The minimization of the amount and cost of fuel results in increased competition and profitability of the companies as well as the reduction of environmental damage. Nowadays, these aspects are particularly important. This research aims to develop a new optimization method for road freight transport costs in order to reduce the fuel costs and determine optimal fueling stations and to calculate the optimal quantity of fuel to refill. The mathematical method developed in this research has two phases. In the first phase the optimal, most cost-effective fuel station is determined based on the potential fuel stations. The specific fuel prices differ per fuel station, and the stations are located at different distances from the main transport way. The method developed in this study supports drivers’ decision-making regarding whether to refuel at a farther but cheaper fuel station or at a nearer but more expensive fuel station based on the more economical choice. Thereafter, it is necessary to determine the optimal fuel volume, i.e., the exact volume required including a safe amount to cover stochastic incidents (e.g., road closures). This aspect of the optimization method supports drivers’ optimal decision-making regarding optimal fuel stations and how much fuel to obtain in order to reduce the fuel cost. Therefore, the application of this new method instead of the recently applied ad-hoc individual decision-making of the drivers results in significant fuel cost savings. A case study confirmed the efficiency of the proposed method.

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