VALIDATION OF THE GEIGER-MULLER COUNTER MODEL OF BDMG-04-02 USING THE MONTE-CARLO TECHNIQUE

The model of the Geiger-Muller counter as the internal part of BDMG-04-02 detection unit in the calibration fa-cility UPGD-2 was developedin MCNP6.2. The different methods are used for the determination of the Geiger-Muller counter response. The F1 and F8 tally applicability is briefly described. BDMG-04-02 model was validated by comparative analysis of the calculated results and experimental values of the counter responses that obtained on the UPGD-2 calibration facility. Additionally, the absolute, geometric and intrinsic registration efficiency of BDMG-04-02 was determined. The paper has been emphasized the disadvantages of using the method of direct counting of the electrons on the surface of the Geiger-Muller counter (F1).

DEVELOPMENT AND APPLICATION OF A CONCENTRATION PROBE FOR MIXING FLOWS TRACKING IN TURBOMACHINERY APPLICATIONS

Modern gas turbines present important temperature distortions in the core-engine flowpath, mainly in the form of hot and cold streaks. As they highly influence turbines performance and lifetime, the precise knowledge of the thermal field evolution through the combustor and the high-pressure turbine is fundamental. The majority of past studies investigated streaks migrations directly examining the thermal field, while a limited amount of experimental work employed approaches based on the detection of tracer gases. The latter approach provides a more detailed evaluation of the evolution and mixing of the different flows. However, the slow time response due to the employment of sampling probes and gas analysers make the investigation extremely time consuming. In this study a commercial oxygen sensor element and its excitation/detection unit were integrated into a newly developed probe to carry out local tracer gas concentration measurements exploiting the fluorescence behaviour. The paper summarizes the probe development and calibration activities, with the characterization of its accuracy for different flow conditions. Finally, two probe applications are described: firstly the probe was used to detect tracer gas concentrations on a jet flow; afterwards it was traversed on the interface plane between a non-reactive, lean combustor simulator and the NGV cascade. The probe has proven to provide accurate and reliable measurements both from a quantitative and qualitative point of view even in highly 3D flow fields typical of gas turbines conditions.

Readout Integrated Circuit for Small-Sized and Low-Power Gas Sensor Based on HEMT Device

This paper presents a small-sized, low-power gas sensor system combining a high-electron-mobility transistor (HEMT) device and readout integrated circuit (ROIC). Using a semiconductor-based HEMT as a gas-sensing device, it is possible to secure high sensitivity, reduced complexity, low power, and small size of the ROIC sensor system. Unlike existing gas sensors comprising only HEMT elements, the proposed sensor system has both an ROIC and a digital controller and can control sensor operation through a simple calibration process with digital signal processing while maintaining constant performance despite variations. The ROIC mainly consists of a transimpedance amplifier (TIA), a negative-voltage generator, and an analog-to-digital converter (ADC) and is designed to match a minimum target detection unit of 1 ppm for hydrogen. The prototype ROIC for the HEMT presented herein was implemented in a 0.18 µm complementary metal–oxide–semiconductor (CMOS) process. The total measured power consumption and detection unit of the proposed ROIC for hydrogen gas were 3.1 mW and 2.6 ppm, respectively.

Development of a Nonenzymatic Electrochemical Sensor for Organophosphate Pesticide Detection Using Copper (II) Oxide Nanorod Electrodes

Inorganic electrode materials of low cost, lower complexity, and high stability have become the more preferred choice over enzyme usage in electrochemical sensors. In this work, copper oxide (CuO) nanorods (NRs) were synthesized on copper foil as electrodes through anodization and annealing processes. The synthesized electrodes were used to analyse the organophosphate pesticides (OPPs) and interference molecules by cyclic voltammetry. The CuO NR sensor was able to identify and quantify different kinds of OPPs with an elevated sensitivity of 1.269, 1.425, 1.657, and 2.833 μA/ng mL-1 for chlorpyrifos, parathion, paraoxon, and pirimiphos and explicitly separate them from interference molecules (i.e., carbaryl, paraquat, sodium nitrate, sodium sulphate, and toluene). Moreover, this electrochemical pesticide sensor achieved a very low limit of detection (LOD) in the 10-7 molar level with a high selectivity among all tested analytes. The LOD for each pesticide ranged from 0.29 to 0.61 μM, revealing the ability to define the maximum residue limit in food. In short, our enzyme-free CuO NR sensor is a promising platform to deliver a fast, low-cost, and reliable pesticide detection unit.

THE EFFECT OF FRAUD ON PROFITABILITY OF LISTED DEPOSIT MONEY BANKS IN NIGERIA

The increasing rate of fraud occurrence and poor profitability rate in the listed Deposit Money Banks (DMBs) in Nigeria calls for a research investigation. To unravel the likely connection between fraud and profitability, this study has examined the effect of fraud on the profitability of listed DMBs in Nigeria. To achieve this objective, the study adopted a correlational research design and utilised secondary data extracted from the Nigerian Deposit Insurance Commission (NDIC) and published financial statements of the DMBs. The study focused on 14 listed DMBs for a six-year period (2012-2017). Panel multiple regression technique was used to estimate the model of the study. The findings showed that fraud (proxied by actual loss from fraud and staff involvement in fraud) has a negative and significant effect on profitability (proxied by return on asset) of listed DMBs in Nigeria. In line with the findings, this study has recommended that listed DMBs should establish fraud detection mechanisms which will entail the setting up of an efficient, reliable and functioning fraud detection unit to monitor transactions that may be susceptible to fraud.

Asynchronous event feature generation and tracking based on gradient descriptor for event cameras

Recently, the event camera has become a popular and promising vision sensor in the research of simultaneous localization and mapping and computer vision owing to its advantages: low latency, high dynamic range, and high temporal resolution. As a basic part of the feature-based SLAM system, the feature tracking method using event cameras is still an open question. In this article, we present a novel asynchronous event feature generation and tracking algorithm operating directly on event-streams to fully utilize the natural asynchronism of event cameras. The proposed algorithm consists of an event-corner detection unit, a descriptor construction unit, and an event feature tracking unit. The event-corner detection unit addresses a fast and asynchronous corner detector to extract event-corners from event-streams. For the descriptor construction unit, we propose a novel asynchronous gradient descriptor inspired by the scale-invariant feature transform descriptor, which helps to achieve quantitative measurement of similarity between event feature pairs. The construction of the gradient descriptor can be decomposed into three stages: speed-invariant time surface maintenance and extraction, principal orientation calculation, and descriptor generation. The event feature tracking unit combines the constructed gradient descriptor and an event feature matching method to achieve asynchronous feature tracking. We implement the proposed algorithm in C++ and evaluate it on a public event dataset. The experimental results show that our proposed method achieves improvement in terms of tracking accuracy and real-time performance when compared with the state-of-the-art asynchronous event-corner tracker and with no compromise on the feature tracking lifetime.

IDOPS, a Profile HMM-Based Tool to Detect Pesticidal Sequences and Compare Their Genetic Context

Biopesticide-based crop protection is constantly challenged by insect resistance. Thus, expansion of available biopesticides is crucial for sustainable agriculture. Although Bacillus thuringiensis is the major agent for pesticide bioprotection, the number of bacteria species synthesizing proteins with biopesticidal potential is much higher. The Bacterial Pesticidal Protein Resource Center (BPPRC) offers a database of sequences for the control of insect pests, grouped in structural classes. Here we present IDOPS, a tool that detects novel biopesticidal sequences and analyzes them within their genetic environment. The backbone of the IDOPS detection unit is a curated collection of high-quality hidden Markov models that is in accordance with the BPPRC nomenclature. IDOPS was positively benchmarked with BtToxin_Digger and Cry_Processor. In addition, a scan of the UniProtKB database using the IDOPS models returned an abundance of new pesticidal protein candidates distributed across all of the structural groups. Gene expression depends on the genomic environment, therefore, IDOPS provides a comparative genomics module to investigate the genetic regions surrounding pesticidal genes. This feature enables the investigation of accessory elements and evolutionary traits relevant for optimal toxin expression and functional diversification. IDOPS contributes and expands our current arsenal of pesticidal proteins used for crop protection.

Background subtraction for night videos

Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training.