scholarly journals Quantitative Monitoring of Selected Groups of Parasites in Domestic Ruminants: A Comparative Review

Pathogens ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1173
Author(s):  
Anna Maurizio ◽  
Antonio Frangipane di Regalbono ◽  
Rudi Cassini
Keyword(s):  
Quantitative Estimation ◽  
Gastrointestinal Nematodes ◽  
Diagnostic Process ◽  
Monitoring And Control ◽  
Main Research ◽  
Quantitative Monitoring ◽  
Comparative Review ◽  
Long Time ◽  
Practical Guidelines ◽  
Liver Flukes

Parasites have had a significant impact on domestic ruminant health and production for a long time, but the emerging threat of drug resistance urgently requires an improved approach to parasite monitoring and control activities. The study reviewed the international literature to analyze the different proposals for the sampling approach and the quantitative estimation of parasite burdens in groups of animals. Moreover, the use of thresholds to decide when and which animal to treat was also investigated. The findings of the study highlighted the presence of a wide-ranging literature on quantitative monitoring for gastrointestinal nematodes (GIN), while more limited data were found for coccidia, and no specific indications were reported for tapeworms. Concerning liver flukes, bronchopulmonary nematodes (BPN) and permanent ectoparasites (lice and mange mites), the diagnostic process is usually aimed at the detection of the parasite rather than at the burden estimation. The main research gaps that need further investigation were also highlighted. For some groups of parasites (e.g., GIN and coccidia) the quantitative approach requires an improved standardization, while its usefulness needs to be confirmed for others (e.g., BPN and lice). The development of practical guidelines for monitoring is also encouraged.

Past Infrastructures and Future Machine Intelligence (MI) for Biofuel Production: A Review and MI-Based Framework

2018 ◽  
Author(s):  
Samuel Hansen ◽  
Amin Mirkouei
Keyword(s):  
Environmental Sustainability ◽  
Alternative Energy ◽  
Ecological Integrity ◽  
Machine Intelligence ◽  
Biofuel Production ◽  
Monitoring And Control ◽  
Economic Resilience ◽  
Capture Process ◽  
Alternative Energy Sources ◽  
Comparative Review

Recent interest in alternative energy sources, particularly biofuels from biomass, is becoming increasingly evident due to energy security and environmental sustainability concerns, such as depletion of conventional energy reserves and carbon footprint effects, respectively. Existing fuels (e.g., biodiesel and ethanol) are neither sustainable nor cost-competitive. There is a need to integrate the recent advanced manufacturing approaches and machine intelligence (MI) techniques (e.g., machine learning and artificial intelligence), targeted on the midstream segment (i.e., pre-/post-conversion processes) of biomass-to-biofuel supply chains (B2BSC). Thus, a comparative review of the existing MI approaches developed in prior studies is performed herein. This review article, additionally, proposes an MI-based framework to enhance productivity and profitability of existing biofuel production processes through intelligent monitoring and control, optimization, and data-driven decision support tools. It is further concluded that a modernized conversion process utilizing MI techniques is essential to seamlessly capture process-level intricacies and enhance techno-economic resilience and socio-ecological integrity of B2BSC.

Probabilistic Risk Analysis of Broken Rail-Caused Train Derailments

2020 ◽  
Author(s):  
Zhipeng Zhang ◽  
Kang Zhou ◽  
Xiang Liu
Keyword(s):  
Risk Model ◽  
Quantitative Estimation ◽  
Low Frequency ◽  
Machine Learning Techniques ◽  
Affecting Factors ◽  
Efficient Manner ◽  
Major Activity ◽  
Long Time ◽  
Cost Efficient ◽  
Railroad Company

Abstract Broken-rail prevention and risk management have been being a major activity for a long time for the railroad industry. The major objective of this research is to evaluate and analyze the broken rail-caused derailment risk using Artificial Intelligence (AI) approaches. The risk model is primarily built upon 1) broken rail probability; 2) probability of broken-rail derailment given a broken rail; and 3) derailment severity, measured by the number of cars derailed. The train derailment risk accounts for derailment probability and derailment consequences simultaneously. Due to the low frequency of broken-rail derailments, it is desirable to estimate the probability of broken rail-caused derailments through the broken rail occurrence. The estimation of the probability of broken rail-caused derailment includes the conditional probability of derailment given broken rail occurrence and the probability of broken rail occurrence. More specially, the probability of broken-rail derailment given a broken rail can be estimated by the statistical relationship between broken-rail derailment and broken rail, given specific variables (e.g., track curvature, signal condition, and annual traffic). The probability of broken rails can be estimated using machine learning techniques based on railroad big data, including maintenance, track layout, traffic and historical inspection records. In terms of derailment consequence, it is defined as the number of cars (both loaded and empty) derailed per derailment that would be estimated based on potentially affecting factors, such as train length, train speed, and train tonnage. The quantitative estimation and analysis of broken rail-caused derailments are based upon the historical records from one Class I railroad company from 2012 to 2016, covering over 20,000 track miles on mainlines. The developed integrated risk model is able to contribute to the prediction of location-centric broken rail-caused derailment risk. Ultimately, the identification of high-risk locations can ultimately aid the railroads to mitigate broken rail risk in a cost-efficient manner and improve railroad safety.

Virtual Instrument Based Control System for the Heating Box

2011 ◽  
Vol 88-89 ◽  
pp. 730-734
Author(s):  
Li Ping Zhang ◽  
Bo Cheng
Keyword(s):  
Control System ◽  
Virtual Instrument ◽  
Heating System ◽  
Monitoring And Control ◽  
Monitoring And Control System ◽  
Vacuum Degree ◽  
Long Time ◽  
And Control ◽  
Interlock System ◽  
Is Design

In some industry and physical systems, work pieces should be baked for a long time before it is used. During heating, the temperature should change according to a line with a given slope, and the vacuum degree should be propriety, so the monitoring and control system is necessary. The monitoring system is used to monitor the vacuum, temperature in the heating system, it can also start the whole system and stop it when the situation is emergent. The control system make the heating system keep a programmed temperature. In this paper, a heating box is design, when the vacuum in the box is lost, the interlock system will turn off turbo pump power supply; at the same time, the control and monitoring systems are designed, the control system is made by PLC and the morning system is constructed by LabVIEW program, and the two systems communicated each other through TCP/IP.

scholarly journals Evaluation of a new semi-automated Hydragel 11 von Willebrand Factor multimers assay kit for routine use

2020 ◽  
Author(s):  
Marika Pikta
Keyword(s):  
Von Willebrand Factor ◽  
Quantitative Estimation ◽  
Von Willebrand Disease ◽  
Diagnostic Process ◽  
Routine Practice ◽  
Significant Difference ◽  
Diagnosis And Classification ◽  
Von Willebrand ◽  
Willebrand Factor

Accurate diagnosis and classification of von Willebrand disease are essential for optimal management.  The von Willebrand factor multimers analysis is in the phenotypic classification, especially in discrepant cases, an integral part of the diagnostic process. The aim of this study was to evaluate the performance of a new Hydragel 11VWF multimer assay (H11VW). Results: Comparison study did not reveal any significant difference between H5VW and H11VW.  The assessment of within-subject results, using H5VW and H11VW, demonstrates the reproducibility of the method in both healthy and VWD patients’ samples collected over time, with identical multimeric pattern on densitometric curves.Conclusion: This assay demonstrated acceptable performance, produced within-day results and can be used in routine practice for the visual investigation of gel and quantitative estimation of VWF multimer fractions.

scholarly journals Seroprevalence of Major Pasture-Borne Parasitoses (Gastrointestinal Nematodes, Liver Flukes and Lungworms) in German Dairy Cattle Herds, Association with Management Factors and Impact on Production Parameters

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2078
Author(s):  
Andrea Springer ◽  
Daniela Jordan ◽  
Alina Kirse ◽  
Bettina Schneider ◽  
Amely Campe ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Milk Fat ◽  
High Performance ◽  
Fasciola Hepatica ◽  
Gastrointestinal Nematodes ◽  
Antibody Detection ◽  
Dual Purpose ◽  
Management Factors ◽  
Cattle Herds ◽  
Liver Flukes

Pasture-borne parasites adversely affect bovine health and productivity worldwide. In Europe, gastrointestinal nematodes, especially Ostertagia ostertagi, the liver fluke Fasciola hepatica and the lungworm Dictyocaulus viviparus represent the most important parasites of dairy cattle. The present study assessed exposure towards these parasites among 646 cattle herds in three parts of Germany during 2017–2019 via antibody detection in bulk tank milk (BTM). Overall, O. ostertagi levels indicative of production losses were detected in 41.2% (266/646; 95% confidence interval (CI): 37.4–45.1%) of BTM samples, while F. hepatica seroprevalence amounted to 14.9% (96/646; 95% CI: 12.2–17.9%). Only 2.3% (15/646; 95% CI: 1.4–3.9%) of samples were D. viviparus antibody-positive. Significantly lower O. ostertagi as well as F. hepatica seroprevalence was detected in dual-purpose breeds compared to high-performance breeds from the same region. Management factors related to parasite exposure included access to fresh grass and hay, silage quality and anthelmintic treatment. Furthermore, F. hepatica and O. ostertagi seropositivity was significantly associated with suboptimal herd-level body condition. Interestingly, the relationship between seropositivity and productivity differed between breed types. Negative impacts on milk yield were detected only in high-performance breeds, while O. ostertagi seropositivity was associated with a lower milk fat content in dual-purpose herds.

scholarly journals PSA Based Biomarkers, Imagistic Techniques and Combined Tests for a Better Diagnostic of Localized Prostate Cancer

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 806
Author(s):  
Vlad Cristian Munteanu ◽  
Raluca Andrada Munteanu ◽  
Diana Gulei ◽  
Vlad Horia Schitcu ◽  
Bogdan Petrut ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Statistical Significance ◽  
Specific Antigen ◽  
Significant Risk ◽  
Digital Rectal Examination ◽  
Localized Prostate Cancer ◽  
Diagnostic Process ◽  
Diagnostic Strategies ◽  
Long Time ◽  
Novel Biomarkers

Prostate cancer represents the most encountered urinary malignancy in males over 50 years old, and the second most diagnosed after lung cancer globally. Digital rectal examination and prostatic specific antigen were the long-time standard tools for diagnosis but with a significant risk of overdiagnosis and overtreatment. Magnetic resonance imaging recently entered the diagnosis process, but to this date, there is no specific biomarker that accurately indicates whether to proceed with the prostate biopsy. Research in this area has gone towards this direction, and recently, serum, urine, imagistic, tissue biomarkers, and Risk Calculators promise to help better diagnose and stratify prostate cancer. In order to eliminate the comorbidities that appear along with the diagnosis and treatment of this disease, there is a constant need to implement new diagnostic strategies. Important uro-oncology associations recommend the use of novel biomarkers in the grey area of prostate cancer, to better distinguish the next step in the diagnostic process. Although it is not that simple, they should be integrated according to the clinical policies, and it should be considered that statistical significance does not always equal clinical significance. In this review, we analyzed the contribution of prostate-specific antigen (PSA)-based biomarkers (PHI, PHID, 4Kscore, STHLM3), imagistic techniques (mp-MRI and mp-US), and combined tests in the early diagnosis process of localized prostate cancer.

scholarly journals Religionsökonomie: eine Übersicht

2012 ◽  
Vol 13 (4) ◽  
pp. 387-406 ◽  
Author(s):  
Janine Höhener ◽  
Christoph A. Schaltegger
Keyword(s):  
Research Area ◽  
Economic Consequences ◽  
Religious Behavior ◽  
Religious Groups ◽  
Main Research ◽  
Economics Of Religion ◽  
Long Time ◽  
Economic Reasoning ◽  
The Individual

AbstractFor a long time, religion has been viewed as a topic outside the range of economics. In the last few decades however, economists increasingly started to extend their methods to non-market-based behavior, including religion. One main research area in the economics of religion concerns the economic consequences of religion both for the individual and for the society at large. The second field of research uses economic reasoning to explain religious behavior of individuals as well as the existence of religious groups. This article provides an overview on this literature and summarizes the main conclusions drawn so far.

scholarly journals Gas Turbine Diagnosis Using a Fault Isolation Enhanced GPA

2004 ◽  
Author(s):  
Y. G. Li
Keyword(s):  
Path Analysis ◽  
Gas Turbine ◽  
Fault Isolation ◽  
Diagnostic Process ◽  
Matching Method ◽  
Performance Simulation ◽  
Turbine Engine ◽  
Turbine Performance ◽  
Path Component ◽  
Long Time

Gas Path Analysis (GPA) and its different derivatives have been developed for more than thirty years and used widely and successfully by many gas turbine manufacturers and operators. In gas turbine gas path component diagnosis, it has been recognized for a long time that GPA would be more successful if degraded components could be located. Unfortunately, only the deviation of measurable parameters is monitored in operation and information about the degraded components is normally not available. In this research, a two-step diagnostic approach is introduced, where a pattern matching method is used first and further developed to isolate degraded components; then Gas Path Analysis is applied to assess the quantity of degradation. A gas turbine performance simulation program, Cranfield University TURBOMATCH, has been modified to simulate the diagnostic process. A model gas turbine engine similar to Rolls-Royce aero AVON is used to test the effectiveness of the approach. It is found that the developed fault isolation method can isolate degraded components accurately and enhance the effectiveness of the quantitative assessment of the degradation with Gas Path Analysis (GPA) in gas turbine diagnostics.

scholarly journals Applying Transfer Learning Using DenseNet121 in Radiographic Image Classification: تطبيق التعلم بالنقل باستخدام شبكة DenseNet121 في تصنيف الصور الشعاعية

2021 ◽  
Vol 5 (4) ◽  
pp. 58-41
Author(s):  
Nahla Saeed Saad Aldeen, Yosser Mohammad Marwan Atassi Nahla Saeed Saad Aldeen, Yosser Mohammad Marwan Atassi
Keyword(s):  
Transfer Learning ◽  
Medical Community ◽  
Diagnostic Process ◽  
Diagnostic Model ◽  
Learning Technology ◽  
Radiographic Images ◽  
Large Group ◽  
Long Time ◽  
Practical Factors ◽  
Fully Connected

The study aims to apply one of the fully connected convolutional neural networks, DenseNet121 network, to a data sample that includes a large group of radiographs through transfer learning technology. Radiography technology is a very important technique in the medical community to detect diseases and abnormalities that may be present, but the interpretation of these images may take a long time and it is subject to error by radiologists who are exposed to external practical factors (such as fatigue resulting from working for long hours, or exhaustion, or thinking about other life matters). To assist radiologists, we have worked on developing a diagnostic model with the help of a deep learning technique to classify radiographic images into two classes: (Normal and Abnormal images), by transferring the selected deep convolutional neural network between a large group of available networks that we studied on the basis of the regions that possibly abnormalities provided by the radiologists for the study sample. We also studied the feasibility of using the well-known VGG16 model on the same data sample and its performance through transfer learning technology and compared its results with the results of the DenseNet121 network. At the end of the research, we obtained a set of good results, which achieved a high diagnostic accuracy of 87.5% in some studied cases, using the DenseNet121 network model, which is considered satisfactory results in the case studied compared to the performance of other models. As for the VGG16 model, it did not give any of the satisfactory results in this field, the accuracy of the classification did not exceed 55% in most cases, and in only two cases it reached about 60% and 62%. The model presented during the research - DenseNet121 model - can be used in the diagnostic process and help in obtaining accurate results in terms of diagnostic results. As for the VGG16 model, it does not give satisfactory results according to the results also obtained during the research, so it is excluded in this type of applications.

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