scholarly journals Response to Comment on “Comparison of Detection Methods of Microplastics in Landfill Mineralized Refuse and Selection of Degradation Degree Indexes”

2022 ◽  
Author(s):  
Ying Zhang ◽  
Yawen Peng ◽  
Chu Peng ◽  
Ping Wang ◽  
Yuan Lu ◽  
...  
Keyword(s):  
Detection Methods ◽  
Degradation Degree ◽  
Selection Of

scholarly journals A Students’ Preferences-Based Approach to Select Methods for Detecting and Handling Free-Riding

2021 ◽  
pp. 027347532199210
Author(s):  
Else-Marie van den Herik ◽  
Tim M. Benning
Keyword(s):  
International Students ◽  
Evaluation System ◽  
Online Survey ◽  
Free Riding ◽  
Detection Methods ◽  
Chi Squared ◽  
Ranking Task ◽  
Handling Methods ◽  
Selection Of ◽  
Marketing Educators

Free-riding is a serious challenge in group projects. While there are various methods to reduce free-riding, marketing educators still face a difficult task when selecting an appropriate method for their course. In this study, we propose a students’ preferences-based approach that supports marketing educators with the selection of methods to detect and handle free-riding. To measure these preferences, students completed an online survey based on a choice task about two methods to detect free-riding and a ranking task about four methods to handle free-riding ( n = 254). Their answers were analyzed using chi-squared tests, Borda scores, and rank-ordered logit models. The results show that (a) neither Dutch nor international students have a clear preference for one of the two detection methods (the reporting system vs. the process evaluation system), (b) grade discussion (a possible reduction of the free-rider’s grade based on a conversation with the course coordinator about each student’s contribution) is the most preferred method to handle free-riding, and (c) international students have a stronger preference for stricter handling methods. Marketing educators can apply the proposed approach, or use our specific findings, for designing methods to reduce free-riding in their courses.

scholarly journals Use of multi-locus sequencing typing as identification method for the food-borne pathogen Listeria monocytogenes: a review

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Sonia Lamon ◽  
Domenico Meloni ◽  
Simonetta Gianna Consolati ◽  
Anna Mureddu ◽  
Rina Mazzette
Keyword(s):  
Listeria Monocytogenes ◽  
Detection Methods ◽  
Causative Organism ◽  
Intracellular Pathogen ◽  
Bacterial Strains ◽  
New Approach ◽  
Food Borne ◽  
Global Epidemiology ◽  
Selection Of

<em>Listeria monocytogenes</em> is an ubiquitous, intracellular pathogen which has been implicated within the past decade as the causative organism in several outbreaks of foodborne diseases. In this review, a new approach to molecular typing primarily designed for global epidemiology has been described: multi-<em>locus</em> sequencing typing (MLST). This approach is novel, in that it uses data that allow the unambiguous characterization of bacterial strains via the Internet. Our aim is to present the currently available selection of references on <em>L. monocytogenes</em> MLST detection methods and to discuss its use as <em>gold</em> <em>standard</em> to <em>L. monocytogenes</em> subtyping method.

scholarly journals Schistosomiasis: from established diagnostic assays to emerging micro/nanotechnology-based rapid field testing for clinical management and epidemiology

2019 ◽  
Vol 3 (1) ◽  
pp. 439-458 ◽  
Author(s):  
Maurice Mutro Nigo ◽  
Georgette Salieb-Beugelaar ◽  
Manuel Battegay ◽  
Peter Odermatt ◽  
Patrick Hunziker
Keyword(s):  
Point Of Care ◽  
Field Testing ◽  
Detection Methods ◽  
Low Grade ◽  
Analytical Strategy ◽  
Diagnostic Assays ◽  
And Control ◽  
Analytical Platforms ◽  
Selection Of

Schistosomiasis is a neglected invasive worm disease with a huge disease burden in developing countries, particularly in children, and is seen increasingly in non-endemic regions through transfer by travellers, expatriates, and refugees. Undetected and untreated infections may be responsible for the persistence of transmission. Rapid and accurate diagnosis is the key to treatment and control. So far, parasitological detection methods remain the cornerstone of Schistosoma infection diagnosis in endemic regions, but conventional tests have limited sensitivity, in particular in low-grade infection. Recent advances contribute to improved detection in clinical and field settings. The recent progress in micro- and nanotechnologies opens a road by enabling the design of new miniaturized point-of-care devices and analytical platforms, which can be used for the rapid detection of these infections. This review starts with an overview of currently available laboratory tests and their performance and then discusses emerging rapid and micro/nanotechnologies-based tools. The epidemiological and clinical setting of testing is then discussed as an important determinant for the selection of the best analytical strategy in patients suspected to suffer from Schistosoma infection. Finally, it discusses the potential role of advanced technologies in the setting near to disease eradication is examined.

scholarly journals Automatic Benchmark Generation Framework for Malware Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Guanghui Liang ◽  
Jianmin Pang ◽  
Zheng Shan ◽  
Runqing Yang ◽  
Yihang Chen
Keyword(s):  
Initial Data ◽  
Malware Detection ◽  
Detection Methods ◽  
Small Data ◽  
Security Threats ◽  
Improved Genetic Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Manual Selection ◽  
Selection Of

To address emerging security threats, various malware detection methods have been proposed every year. Therefore, a small but representative set of malware samples are usually needed for detection model, especially for machine-learning-based malware detection models. However, current manual selection of representative samples from large unknown file collection is labor intensive and not scalable. In this paper, we firstly propose a framework that can automatically generate a small data set for malware detection. With this framework, we extract behavior features from a large initial data set and then use a hierarchical clustering technique to identify different types of malware. An improved genetic algorithm based on roulette wheel sampling is implemented to generate final test data set. The final data set is only one-eighteenth the volume of the initial data set, and evaluations show that the data set selected by the proposed framework is much smaller than the original one but does not lose nearly any semantics.

scholarly journals A non-lethal method for detection of Bonamia ostreae in flat oyster (Ostrea edulis) using environmental DNA

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Louise von Gersdorff Jørgensen ◽  
Johan Wedel Nielsen ◽  
Mikkel Kehler Villadsen ◽  
Bent Vismann ◽  
Sussie Dalvin ◽  
...  
Keyword(s):  
Water Samples ◽  
Sea Water ◽  
Diagnostic Technique ◽  
Environmental Dna ◽  
Detection Methods ◽  
Ostrea Edulis ◽  
Breeding Programs ◽  
Bonamia Ostreae ◽  
Flat Oyster ◽  
Selection Of

Abstract Surveillance and diagnosis of parasitic Bonamia ostreae infections in flat oysters (Ostrea edulis) are prerequisites for protection and management of wild populations. In addition, reliable and non-lethal detection methods are required for selection of healthy brood oysters in aquaculture productions. Here we present a non-lethal diagnostic technique based on environmental DNA (eDNA) from water samples and demonstrate applications in laboratory trials. Forty oysters originating from Limfjorden, Denmark were kept in 30 ppt sea water in individual tanks. Water was sampled 6 days later, after which all oysters were euthanized and examined for infection, applying PCR. Four oysters (10%) were found to be infected with B. ostreae in gill and mantle tissue. eDNA purified from the water surrounding these oysters contained parasite DNA. A subsequent sampling from the field encompassed 20 oysters and 15 water samples from 5 different locations. Only one oyster turned out positive and all water samples proved negative for B. ostreae eDNA. With this new method B. ostreae may be detected by only sampling water from the environment of isolated oysters or isolated oyster populations. This non-lethal diagnostic eDNA method could have potential for future surveys and oyster breeding programs aiming at producing disease-free oysters.

scholarly journals Methodology of machine destruction testing

2018 ◽  
Vol 182 ◽  
pp. 01007
Author(s):  
Vladimir Boykov ◽  
Aleksandr Povarecho
Keyword(s):  
Data Processing ◽  
Current Knowledge ◽  
Good Prognosis ◽  
Vibration Method ◽  
Diagnostic Parameters ◽  
Operation And Maintenance ◽  
Technical Systems ◽  
Degradation Degree ◽  
Automated Procedures ◽  
Selection Of

This paper presents selected problems connected with automation of procedures involved in assessment of machine degradation degree using vibration method with special emphasis on the machine state prognosis. The current knowledge of these problems is not sufficient and needs further research on data processing, analysis of efficiency of diagnostic and prognostic procedures, collection and selection of diagnostic parameters and development of automatic procedures for recognition and prognosis of a machine state. New solutions and different aspects of diagnostic prognosis based on the proposed partial procedures focus on factors determining automation of procedures for identification of technical systems states. New automated procedures for acquisition and processing of symptoms indicating the machine state provide better possibilities of control and supervision of technical systems operation and maintenance through identification of their current states, and its good prognosis.

scholarly journals Comparison of the Performances of Two Aptamers on a Colorimetric Assay for the Quantification of Fumonisin B1

Proceedings ◽  
2020 ◽  
Vol 60 (1) ◽  
pp. 19
Author(s):  
Vicente Antonio Mirón-Mérida ◽  
Yadira González-Espinosa ◽  
Yun Yun Gong ◽  
Yuan Guo ◽  
Francisco M. Goycoolea
Keyword(s):  
Limit Of Detection ◽  
Colorimetric Assay ◽  
Fusarium Verticillioides ◽  
Fumonisin B1 ◽  
Detection Methods ◽  
Field Flow Fractionation ◽  
Asymmetric Flow ◽  
Subsequent Addition ◽  
Selection Of ◽  
Analytical Detection

Fumonisin B1 (FB1), a mycotoxin commonly produced by Fusarium verticillioides and classified as a group 2B hazard, has been identified in various food products; hence, sensitive and rapid analytical detection methods are needed. Since the first reported aptamer (96 nt ssDNA) for the highly specific molecular recognition of FB1, only 30 aptamer-based biosensors have been published. A critical point, yet commonly overlooked during the design of aptasensors, is the selection of the binding buffer. In this work, a colorimetric assay was designed by incubating a folded aptamer with FB1 and the subsequent addition of gold nanoparticles (AuNPs). The changes in the aggregation profile of AuNPs by a 40 nt aptamer and a 96 nt aptamer were tested after the addition of FB1 under different buffer conditions, where the incubation with Tris-HCl and MgCl2 exhibited the most favorable performances. The assay with the longest aptamer was specific to FB1 and comparable to other aptasensors with a limit of detection (LOD) of 3 ng/mL (A650/520 ratio). Additionally, the application of asymmetric-flow field-flow fractionation (AF4) with multidetection allowed for the analysis of the peak area (λ) and multi-angle light scattering (MALS) with LODs of up to the fg/mL level.

scholarly journals Recognition of Acoustic Signals of Commutator Motors

2018 ◽  
Vol 8 (12) ◽  
pp. 2630 ◽  
Author(s):  
Adam Glowacz
Keyword(s):  
Fault Detection ◽  
Extraction Method ◽  
Acoustic Signals ◽  
Rotor Blades ◽  
Gear Train ◽  
Detection Methods ◽  
Support Vector ◽  
Total Efficiency ◽  
Feature Extraction Method ◽  
Selection Of

Most faults can stop a motor, and time is lost in fixing the damaged motor. This is a reason why it is essential to develop fault-detection methods. This paper describes the acoustic-based fault detection of two commutator motors: the commutator motor of an electric impact drill and the commutator motor of a blender. Acoustic signals were recorded by a smartphone. Five states of the electric impact drill and three states of the blender were analysed: for the electric impact drill, these states were healthy, damaged gear train, faulty fan with five broken rotor blades, faulty fan with 10 broken rotor blades, and shifted brush (motor off); for the blender, these states were healthy, faulty fan with two broken rotor blades, and faulty fan with five broken rotor blades. A feature extraction method, MSAF-RATIO-27-MULTIEXPANDED-4-GROUPS (Method of Selection of Amplitudes of Frequency Ratio of 27% Multiexpanded 4 Groups), was developed and used for the computation of feature vectors. The nearest mean (NM) and support vector machine (SVM) classifiers were used for data classification. Analysis of the recognition of acoustic signals was carried out. The analysed value of TEEID (the total efficiency of recognition of the electric impact drill) was equal to 96% for the NM classifier and 88.8% for SVM. The analysed value of TEB (the total efficiency of recognition of the blender) was equal to 100% for the NM classifier and 94.11% for SVM.

Sign in / Sign up

Export Citation Format

Share Document