scholarly journals Statistical analysis for explosives detection system test and evaluation

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefan Lukow ◽  
James C. Weatherall

AbstractThe verification of trace explosives detection systems is often constrained to small sample sets, so it is important to support the significance of the results with statistical analysis. As binary measurements, the trials are assessed using binomial statistics. A method is described based on the probability confidence interval and expressed in terms of the upper confidence interval bound that reports the probability of successful detection and its level of statistical confidence. These parameters provide useful measures of the system’s performance. The propriety of combining statistics for similar tests—for example in trace detection trials of an explosive on multiple surfaces—is examined by statistical tests. The use of normal statistics is commonly applied to binary testing, but the confidence intervals are known to behave poorly in many circumstances, including small sample numbers. The improvement of the normal approximation with increasing sample number is shown not to be substantial for the typical numbers used in this type of explosives detection system testing, and binary statistics are preferred. The methods and techniques described here for testing trace detection can be applied as well to performance testing of explosives detection systems in general.

1972 ◽  
Vol 11 (61) ◽  
pp. 73-79 ◽  
Author(s):  
R. E. Dugdale

AbstractData from Norwegian glaciers and statistical tests are presented which suggest that vertical net-budget gradient, ablation gradient and equilibrium-line altitude can be taken as characteristic for any particular glacier. The usefulness of these conceptual models as predictive techniques for the regional determination of glacier net budget when only a small sample is available, and in palaeo-net-budget studies, is shown to be limited.


1972 ◽  
Vol 11 (61) ◽  
pp. 73-79 ◽  
Author(s):  
R. E. Dugdale

Abstract Data from Norwegian glaciers and statistical tests are presented which suggest that vertical net-budget gradient, ablation gradient and equilibrium-line altitude can be taken as characteristic for any particular glacier. The usefulness of these conceptual models as predictive techniques for the regional determination of glacier net budget when only a small sample is available, and in palaeo-net-budget studies, is shown to be limited.


Author(s):  
Ahmad Iwan Fadli ◽  
Selo Sulistyo ◽  
Sigit Basuki Wibowo

Driving accidents are serious events that could cause fatality. According to WHO’s reports, reckless driving behaviors such as speeding, driving under influence, and operating phones while driving are among the main factors that could reduce the focus of drivers while driving. Driving accidents are also difficult to handle on a large scale in a country. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. Drivers tend to be more careful when they know that their driving behaviors are being monitored. We created a classifier model that can be applied to detection systems to classify whether a driver is driving safely or not safely using travel sensor data, which includes gyroscope, accelerometer, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP). This study shows that RF has the best performance with 98% accuracy, 98% precision, and sensitivity 97%. Performance testing shows that the proposed pre-processing method can increase the classifier sensitivity value in the research dataset. It is hoped that the classifier model can be applied to the driving detection system so that it can reduce the risk of traffic accidents.


2019 ◽  
Vol 129 (4) ◽  
pp. 127-131
Author(s):  
Agnieszka Parfin ◽  
Krystian Wdowiak ◽  
Marzena Furtak-Niczyporuk ◽  
Jolanta Herda

AbstractIntroduction. The COVID-19 is the name of an infectious disease caused by a new strain of coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). It was first diagnosed in December 2019 in patients in Wuhan City, Hubei Province, China. The symptoms are dominated by features of respiratory tract infections, in some patients with a very severe course leading to respiratory failure and, in extreme cases to death. Due to the spread of the infection worldwide, the WHO declared a pandemic in March 2020.Aim. An investigation of the impact of social isolation introduced due to the coronavirus pandemic on selected aspects of life. The researchers focused on observing changes in habits related to physical activity and their connections with people’s subjective well-being and emotional state.Material and methods. The study was carried out within the international project of the group „IRG on COVID and exercise”. The research tool was a standardized questionnaire.Results. Based on the data collected and the analysis of the percentage results, it can be observed that the overwhelming majority of people taking up physical activity reported a better mood during the pandemic. However, statistical tests do not confirm these relationships due to the small sample size.Conclusions. Isolation favours physical activity. Future, in-depth studies, by enlarging the population group, are necessary to confirm the above observations.


2020 ◽  
Author(s):  
Hideya Kawasaki ◽  
Hiromi Suzuki ◽  
Masato Maekawa ◽  
Takahiko Hariyama

BACKGROUND As pathogens such as influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can easily cause pandemics, rapid diagnostic tests are crucial for implementing efficient quarantine measures, providing effective treatments to patients, and preventing or containing a pandemic infection. Here, we developed the immunochromatography-NanoSuit® method, an improved immunochromatography method combined with a conventional scanning electron microscope (SEM), which enables observation of immunocomplexes labeled with a colloidal metal. OBJECTIVE A total of 197 clinical samples from patients suspected to be suffering from influenza were provided by a general hospital at the Hamamatsu University School of Medicine for examination using the Flu kit. METHODS Immunochromatography kit The ImunoAce® Flu kit (NP antigen detection), a human influenza commercial diagnosis kit, was purchased from TAUNS Laboratories, Inc. (Shizuoka, Japan). Au/Pt nanoparticles were utilized to visualize the positive lines. A total of 197 clinical samples from patients suspected to be suffering from influenza were provided by a general hospital at the Hamamatsu University School of Medicine for examination using the Flu kit. After macroscopic diagnosis using the Flu kit, the samples were stored in a biosafety box at room temperature (20-25 °C / 68 - 77 °F). The IgM detection immunochromatography kit against SARS-CoV-2 was obtained from Kurabo Industries, Ltd. (Osaka, Japan). One step rRT-PCR for influenza A rRT-PCR for influenza A was performed as described previously using Flu A universal primers. A Ct within 38.0 was considered as positive according to the CDC protocol. The primer/probe set targeted the human RNase P gene and served as an internal control for human nucleic acid as described previously. SEM image acquisition The immunochromatography kit was covered with a modified NanoSuit® solution based on previously published components (Nisshin EM Co., Ltd., Tokyo, Japan), placed first onto the wide stage of the specimen holder, and then placed in an Lv-SEM (TM4000Plus, Hitachi High-Technologies, Tokyo, Japan). Images were acquired using backscattered electron detectors with 10 or 15 kV at 30 Pa. Particle counting In fields containing fewer than 50 particles/field, the particles were counted manually. Otherwise, ImageJ/Fiji software was used for counting. ImageJ/Fiji uses comprehensive particle analysis algorithms that effectively count various particles. Images were then processed and counting was performed according to the protocol. Diagnosis and statistics The EM diagnosis and criteria for a positive test were defined as follows: particle numbers from 6 fields from the background area and test-line were statistically analyzed using the t-test. If there were more than 5 particles in one visual field and a significant difference (P < 0.01) was indicated by the t-test, the result was considered positive. Statistical analysis using the t-test was performed in Excel software. Statistical analysis of the assay sensitivity and specificity with a 95% confidence interval (95% CI) was performed using the MedCalc statistical website. The approximate line, correlation coefficient, and null hypothesis were calculated with Excel software. RESULTS Our new immunochromatography-NanoSuit® method suppresses cellulose deformity and makes it possible to easily focus and acquire high-resolution images of gold/platinum labeled immunocomplexes of viruses such as influenza A, without the need for conductive treatment as with conventional SEM. Electron microscopy (EM)-based diagnosis of influenza A exhibited 94% clinical sensitivity (29/31) (95% confidence interval [95%CI]: 78.58–99.21%) and 100% clinical specificity (95%CI: 97.80–100%). EM-based diagnosis was significantly more sensitive (71.2%) than macroscopic diagnosis (14.3%), especially in the lower influenza A-RNA copy number group. The detection ability of our method is comparable to that of real-time reverse transcription-polymerase chain reaction. CONCLUSIONS This simple and highly sensitive quantitative analysis method involving immunochromatography can be utilized to diagnose various infections in humans and livestock, including highly infectious diseases such as COVID-19.


2021 ◽  
Vol 9 ◽  
pp. 205031212110202
Author(s):  
Rgda Mohamed Osman ◽  
Mounkaila Noma ◽  
Abdallah Elssir Ahmed ◽  
Hanadi Abdelbagi ◽  
Rihab Ali Omer ◽  
...  

Objectives: Rheumatoid arthritis is a chronic inflammatory autoimmune disease. This study aimed to determine the association of interleukin-17A-197G/A polymorphism with rheumatoid arthritis in Sudanese patients. Methods: A case–control study was conducted between March and December 2018. Clinical and demographic data of the study participants were collected and analyzed. Polymerase chain reaction restriction fragment length polymorphism molecular technique was done to investigate interleukin-17A-197G/A polymorphisms. All statistical tests were considered statistically significant when p < 0.05. Results: The study population included 266 participants aged between 1 and 85 years, with an average of 40 years, classified into 85 (31.2%) cases (mean age 48.5 ± 11.3 years), and 181 (68.8%) controls (mean age 35.3 ± 15.9 years). The interleukin-17A homozygote AA genotype was more frequent among the control group compared to the case group; 95 (52.5%) and 7 (8.2%), respectively. The homozygote GG and the heterozygote AG genotypes were proportionally not different among the cases and control groups; 13 (54.2%) and 11 (45.8%), and 65 (46.4%) and 75 (53.6%), respectively. According to the distribution of interleukin-17A genotypes, a statistically significant difference was observed among cases with the interleukin-17A AA and AG genotypes, p values 0.001 and 0.004, respectively. For the association interleukin-17A genotypes and family history a negatively significant association was reported (95% confidence interval, –0.219, p value = 0.001). There was also a negatively significant association of interleukin-17A genotypes and anti-cyclic citrullinated peptide (95% confidence interval, −0.141, p value = 0.002). Conclusion: This study is the first study in Sudan established the association between interleukin-17A-197G/A (rs2275913) polymorphisms and susceptibly to rheumatoid arthritis. These findings appeal for further research in Sudan to investigate the exact role of IL-17A in immunopathology and disease severity among Sudanese rheumatoid arthritis


Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4097-4108 ◽  
Author(s):  
Moustafa Ahmed ◽  
Yas Al-Hadeethi ◽  
Ahmed Bakry ◽  
Hamed Dalir ◽  
Volker J. Sorger

AbstractThe technologically-relevant task of feature extraction from data performed in deep-learning systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in prevalent domain-specific architectures such as in graphics processing units (GPU). However, electronics systems are limited with respect to power dissipation and delay, due to wire-charging challenges related to interconnect capacitance. Here we present a silicon photonics-based architecture for convolutional neural networks that harnesses the phase property of light to perform FFTs efficiently by executing the convolution as a multiplication in the Fourier-domain. The algorithmic executing time is determined by the time-of-flight of the signal through this photonic reconfigurable passive FFT ‘filter’ circuit and is on the order of 10’s of picosecond short. A sensitivity analysis shows that this optical processor must be thermally phase stabilized corresponding to a few degrees. Furthermore, we find that for a small sample number, the obtainable number of convolutions per {time, power, and chip area) outperforms GPUs by about two orders of magnitude. Lastly, we show that, conceptually, the optical FFT and convolution-processing performance is indeed directly linked to optoelectronic device-level, and improvements in plasmonics, metamaterials or nanophotonics are fueling next generation densely interconnected intelligent photonic circuits with relevance for edge-computing 5G networks by processing tensor operations optically.


Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2674
Author(s):  
Qingying Ren ◽  
Wen Zuo ◽  
Jie Xu ◽  
Leisheng Jin ◽  
Wei Li ◽  
...  

At present, the proposed microwave power detection systems cannot provide a high dynamic detection range and measurement sensitivity at the same time. Additionally, the frequency band of these detection systems cannot cover the 5G-communication frequency band. In this work, a novel microwave power detection system is proposed to measure the power of the 5G-communication frequency band. The detection system is composed of a signal receiving module, a power detection module and a data processing module. Experiments show that the detection frequency band of this system ranges from 1.4 GHz to 5.3 GHz, the dynamic measurement range is 70 dB, the minimum detection power is −68 dBm, and the sensitivity is 22.3 mV/dBm. Compared with other detection systems, the performance of this detection system in the 5G-communication frequency band is significantly improved. Therefore, this microwave power detection system has certain reference significance and application value in the microwave signal detection of 5G communication systems.


Author(s):  
Nicole Gailey ◽  
Noman Rasool

Canada and the United States have vast energy resources, supported by thousands of kilometers (miles) of pipeline infrastructure built and maintained each year. Whether the pipeline runs through remote territory or passing through local city centers, keeping commodities flowing safely is a critical part of day-to-day operation for any pipeline. Real-time leak detection systems have become a critical system that companies require in order to provide safe operations, protection of the environment and compliance with regulations. The function of a leak detection system is the ability to identify and confirm a leak event in a timely and precise manner. Flow measurement devices are a critical input into many leak detection systems and in order to ensure flow measurement accuracy, custody transfer grade liquid ultrasonic meters (as defined in API MPMS chapter 5.8) can be utilized to provide superior accuracy, performance and diagnostics. This paper presents a sample of real-time data collected from a field install base of over 245 custody transfer grade liquid ultrasonic meters currently being utilized in pipeline leak detection applications. The data helps to identify upstream instrumentation anomalies and illustrate the abilities of the utilization of diagnostics within the liquid ultrasonic meters to further improve current leak detection real time transient models (RTTM) and pipeline operational procedures. The paper discusses considerations addressed while evaluating data and understanding the importance of accuracy within the metering equipment utilized. It also elaborates on significant benefits associated with the utilization of the ultrasonic meter’s capabilities and the importance of diagnosing other pipeline issues and uncertainties outside of measurement errors.


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