Assignment-free chirality detection in unknown samples via microwave three-wave mixing

Straightforward identification of chiral molecules in multi-component mixtures of unknown composition is extremely challenging. Current spectrometric and chromatographic methods cannot unambiguously identify components while the state of the art spectroscopic methods are limited by the difficult and time-consuming task of spectral assignment. Here, we introduce a highly sensitive generalized version of microwave three-wave mixing that uses broad-spectrum fields to detect chiral molecules in enantiomeric excess without any prior chemical knowledge of the sample. This method does not require spectral assignment as a necessary step to extract information out of a spectrum. We demonstrate our method by recording three-wave mixing spectra of multi-component samples that provide direct evidence of enantiomeric excess. Our method opens up new capabilities in ultrasensitive phase-coherent spectroscopic detection that can be applied for chiral detection in real-life mixtures, raw products of chemical reactions and difficult to assign novel exotic species.

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A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation

Metals ◽

10.3390/met11060870 ◽

2021 ◽

Vol 11 (6) ◽

pp. 870

Author(s):

Robby Neven ◽

Toon Goedemé

Keyword(s):

Process Parameters ◽

Visual Inspection ◽

Steel Surface ◽

Surface Defect ◽

State Of The Art ◽

Sheet Steel ◽

Real Life ◽

Single Task ◽

Severity Estimation ◽

Task Model

Automating sheet steel visual inspection can improve quality and reduce costs during its production. While many manufacturers still rely on manual or traditional inspection methods, deep learning-based approaches have proven their efficiency. In this paper, we go beyond the state-of-the-art in this domain by proposing a multi-task model that performs both pixel-based defect segmentation and severity estimation of the defects in one two-branch network. Additionally, we show how incorporation of the production process parameters improves the model’s performance. After manually constructing a real-life industrial dataset, we first implemented and trained two single-task models performing the defect segmentation and severity estimation tasks separately. Next, we compared this to a multi-task model that simultaneously performs the two tasks at hand. By combining the tasks into one model, both segmentation tasks improved by 2.5% and 3% mIoU, respectively. In the next step, we extended the multi-task model using sensor fusion with process parameters. We demonstrate that the incorporation of the process parameters resulted in a further mIoU increase of 6.8% and 2.9% for the defect segmentation and severity estimation tasks, respectively.

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New Multi-View Classification Method with Uncertain Data

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3458282 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-23

Author(s):

Bo Liu ◽

Haowen Zhong ◽

Yanshan Xiao

Keyword(s):

Learning Strategy ◽

State Of The Art ◽

Uncertain Data ◽

Real Life ◽

Support Vector ◽

Classification Methods ◽

Complementary Information ◽

Novel Approach ◽

Svm Model ◽

Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

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Drone Deep Reinforcement Learning: A Review

Electronics ◽

10.3390/electronics10090999 ◽

2021 ◽

Vol 10 (9) ◽

pp. 999

Author(s):

Ahmad Taher Azar ◽

Anis Koubaa ◽

Nada Ali Mohamed ◽

Habiba A. Ibrahim ◽

Zahra Fathy Ibrahim ◽

...

Keyword(s):

Reinforcement Learning ◽

State Of The Art ◽

Real Life ◽

Environment Monitoring ◽

Simulated Environments ◽

Infrastructure Inspection ◽

Remote Sensing Mapping ◽

And Control ◽

The Military ◽

Uav Navigation

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.

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Fabry Disease Therapy: State-of-the-Art and Current Challenges

International Journal of Molecular Sciences ◽

10.3390/ijms22010206 ◽

2020 ◽

Vol 22 (1) ◽

pp. 206

Author(s):

Olga Azevedo ◽

Miguel Fernandes Gago ◽

Gabriel Miltenberger-Miltenyi ◽

Nuno Sousa ◽

Damião Cunha

Keyword(s):

Fabry Disease ◽

State Of The Art ◽

Large Body ◽

Real Life ◽

Clinical Manifestations ◽

Substrate Reduction Therapy ◽

Lysosomal Storage ◽

New Therapies ◽

Enzyme Replacement

Fabry disease (FD) is a lysosomal storage disorder caused by mutations of the GLA gene that lead to a deficiency of the enzymatic activity of α-galactosidase A. Available therapies for FD include enzyme replacement therapy (ERT) (agalsidase alfa and agalsidase beta) and the chaperone migalastat. Despite the large body of literature published about ERT over the years, many issues remain unresolved, such as the optimal dose, the best timing to start therapy, and the clinical impact of anti-drug antibodies. Migalastat was recently approved for FD patients with amenable GLA mutations; however, recent studies have raised concerns that “in vitro” amenability may not always reflect “in vivo” amenability, and some findings on real-life studies have contrasted with the results of the pivotal clinical trials. Moreover, both FD specific therapies present limitations, and the attempt to correct the enzymatic deficiency, either by enzyme exogenous administration or enzyme stabilization with a chaperone, has not shown to be able to fully revert FD pathology and clinical manifestations. Therefore, several new therapies are under research, including new forms of ERT, substrate reduction therapy, mRNA therapy, and gene therapy. In this review, we provide an overview of the state-of-the-art on the currently approved and emerging new therapies for adult patients with FD.

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Diagnosis of spontaneous intracranial hypotension by using magnetic resonance myelography

Journal of Neurosurgery ◽

10.3171/jns.2000.92.5.0873 ◽

2000 ◽

Vol 92 (5) ◽

pp. 873-876 ◽

Cited By ~ 45

Author(s):

Akira Matsumura ◽

Izumi Anno ◽

Hiroshi Kimura ◽

Eiichi Ishikawa ◽

Tadao Nose

Keyword(s):

Magnetic Resonance ◽

Intracranial Hypotension ◽

Direct Evidence ◽

Spontaneous Intracranial Hypotension ◽

Content Type ◽

Highly Sensitive ◽

Csf Leakage ◽

Leakage Site ◽

A Site ◽

Mr Myelography

✓ The authors describe a case of spontaneous intracranial hypotension in which the leakage site was determined by using magnetic resonance (MR) myelography. This technique demonstrated the route of cerebrospinal fluid (CSF) leakage, whereas other methods failed to show direct evidence of leakage. Magnetic resonance myelography is a noninvasive method that is highly sensitive in detecting CSF leakage. This is the first report in which a site of CSF leakage was detected using MR myelography.

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SOSerbia: Android-Based Software Platform for Sending Emergency Messages

Complexity ◽

10.1155/2018/8283919 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9

Author(s):

Mihailo Jovanovic ◽

Ivan Babic ◽

Milan Cabarkapa ◽

Jelena Misic ◽

Sasa Mijalkovic ◽

...

Keyword(s):

Emergency Response ◽

State Of The Art ◽

Complete Solution ◽

Real Life ◽

Android Application ◽

Unique Combination ◽

Background Process ◽

Life Problems ◽

The Republic ◽

National Police

This paper presents Android-based SOS platform named SOSerbia for sending emergency messages by citizens in Serbia. The heart of the platform is SOS client Android application which is an easy and simple solution for sending SOS messages with unique combination of volume buttons. The proposed platform solves a lot of safety, security, and emergency problems for people who can be in dangerous situations. After a person presses a correct combination of buttons, a message with his or her location is sent to the operating center of the Serbian Police. The platform merges several appropriately combined advanced Android technologies into one complete solution. The proposed solution also uses the Google location API for getting user’s location and Media Player broadcast receiver for reading pressed buttons for volume. This logic can be also customized for any other mobile operating system. In other words, the proposed architecture can be also implemented in iOS or Windows OS. It should be noted that the proposed architecture is optimized for different mobile devices. It is also implemented with simple widget and background process based on location. The proposed platform is experimentally demonstrated as a part of emergency response center at the Ministry of Interior of the Republic of Serbia. This platform overcomes real-life problems that other state-of-the-art solutions introduce and can be applied and integrated easily in any national police and e-government systems.

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A new twist in eutectic composition: deracemization of a racemic compound amino acid by Viedma ripening and temperature fluctuation.

10.33774/chemrxiv-2021-38xnd-v4 ◽

2021 ◽

Author(s):

CRISTOBAL VIEDMA ◽

José Eugenio Ortíz

Keyword(s):

Temperature Fluctuation ◽

Solid Phase ◽

Enantiomeric Excess ◽

Eutectic Mixture ◽

Chiral Molecules ◽

Racemic Compound ◽

Chiral Compounds ◽

First Case ◽

New Finding ◽

New Ideas

Viedma ripening and temperature fluctuation are processes based on solution phase racemization and dissolution-growth of racemic or scalemic conglomerates resulting in solid-phase deracemization. The dissolution-growth process is performed by abrasive grinding in the first case and by the temperature fluctuation in the second. But both methods have the intrinsic drawback of being only applicable to conglomerates, accounting for only 10% of all chiral molecules and are not suitable for the 90% of chiral compounds that crystallize as racemic compound. Herein we show that the enantiomeric excess of the solution in the eutectic mixture formed by a racemic compound and one of its enantiomers in suspension changes dramatically by growth-dissolution of the crystals through grinding and temperature fluctuation, converting the racemic compound into the desired enantiomer. With this new finding the scope of Viedma ripening and temperature fluctuation could be significantly expanded and can shed new ideas about the origin of biological homochirality on earth.

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Enhancement of Electrocatalytic Oxygen Evolution by Chiral Molecular Functionalization of Hybrid 2D Electrodes

10.33774/chemrxiv-2021-gmp8j ◽

2021 ◽

Author(s):

Yunchang Liang ◽

Karla Banjac ◽

Kévin Martin ◽

Nicolas Zigon ◽

Seunghwa Lee ◽

...

Keyword(s):

Binding Energy ◽

Oxygen Evolution ◽

State Of The Art ◽

Chiral Molecules ◽

Surface Binding ◽

Surface Binding Energy ◽

Energy Conversion And Storage ◽

Polarization Mechanism ◽

And Storage ◽

Unconventional Methods

A sustainable future requires highly efficient energy conversion and storage processes, where electrocatalysis plays a crucial role. The activity of an electrocatalyst is governed by the binding energy towards the reaction intermediates, while the scaling relationships prevent the improvement of a catalytic system over its volcano-plot limits. To overcome these limitations, unconventional methods that are not fully determined by the surface binding energy can be helpful. Here, we use organic chiral molecules, i.e., hetero-helicenes, to boost the oxygen evolution reaction (OER) by ca. 131.5 % (at the potential of 1.65 V vs. RHE) at state-of-the-art 2D catalysts via a spin-polarization mechanism. Our results show that chiral molecule-functionalization is able to increase the OER activity of catalysts beyond the volcano limits. A guideline for optimizing the catalytic activity via chiral molecular functionalization of hybrid 2D electrodes is given.

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A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

Applied Sciences ◽

10.3390/app11178074 ◽

2021 ◽

Vol 11 (17) ◽

pp. 8074

Author(s):

Tierui Zou ◽

Nader Aljohani ◽

Keerthiraj Nagaraj ◽

Sheng Zou ◽

Cody Ruben ◽

...

Keyword(s):

Machine Learning ◽

Power Systems ◽

State Of The Art ◽

Real Life ◽

The State ◽

Wide Area ◽

Network Parameter ◽

False Data Injection ◽

Network Parameters ◽

Injection Attacks

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

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