Optimising Vine Weevil, Otiorhynchus sulcatus F. (Coleoptera: Curculionidae), Monitoring Tool Design

Vine weevil, Otiorhynchus sulcatus F. (Coleoptera: Curculionidae), is an economically important insect pest of horticultural crops. To identify an effective and reliable monitoring system for adult vine weevil, this study investigated the influence of colour, height and entrance position on the efficacy of a model monitoring tool using modified paper cups as refuges. Vine weevil preferences were determined by the number of individuals recorded within a refuge. When provided with a binary choice between black or white refuges, vine weevil adults showed a preference for black refuges. Vine weevils provided with a range of coloured refuges (blue, green, red and yellow) in addition to black and white refuges showed a preference for black and blue over the other colours and white refuges in group choice experiments. Refuge height and entrance position also influenced vine weevil behaviour with individuals exhibiting a preference for taller refuges and those with entrance openings around the refuge base. These results provide insights into refuge selection by adult vine weevils, which can be exploited to improve monitoring tool design. The importance of developing an effective monitoring tool for vine weevil adults as part of an integrated pest management programme is discussed.

Pesticides Use and Impaired Lung Function Among Male Agricultural Farmers in Rural Sindh, Pakistan

Use of pesticides implicated in decrement in lung function, manifested as restrictive or obstructive dysfunction or both. Using comparative cross-sectional study design, authors conducted the study to determine an association of pesticide use with impairment in lung function (restrictive/obstructive pattern of lung function) among agricultural farmers. The data were collected using modified American Thoracic Society Division of Lung Disease (ATS-DLD-78A) questionnaire and spirometer measurements. Using multinomial logistic regression, the risk of both restrictive and obstructive lung dysfunction was found to be almost twice among pesticides users with each increasing year of exposure to pesticides (1.92 and 1.95, respectively) after adjusting for other covariates. There is a need for reliable monitoring and reporting procedures along with appropriate environmental policies and regulations for handling of pesticides. Interventional studies are needed where farmers could be trained on the proper use of personal protective equipment (PPE) to limit the exposure to pesticides.

Development of the dual-channel frequency meter for measurements with hydrostatic pressure sensor

The paper describes the process and the results of development of the dual-channel frequency meter, which function is to measure the output frequency generated by bottom-mounted pressure sensors. The sensors are actively used to monitor the marine environment. AVR family microcontrollers were used as the computing core of the presented device. This solution allows to obtain far lower power consumption, which is especially important when operating with no industrial power supply system in the coastal zone. As a result, we can deploy a reliable monitoring equipment capable of long-term saving data and if necessary transmit it for further processing. The developed frequency-meter is able to continually record the ambient temperature, atmospheric pressure and dynamically varying output frequency, which depends on hydrostatic pressure (sea level). To obtain more accurate data, we implemented a frequency measure method called reciprocal counter with lower relative error not affected by value of the output frequency. A laboratory experiment has been conducted, which confirms the suitability of the developed frequency meter for field-oriented conditions.

Artificial Intelligence Approach to the Monitoring of Respiratory Sounds in Asthmatic Patients

Background: Effective and reliable monitoring of asthma at home is a relevant factor that may reduce the need to consult a doctor in person.Aim: We analyzed the possibility to determine intensities of pathological breath phenomena based on artificial intelligence (AI) analysis of sounds recorded during standard stethoscope auscultation.Methods: The evaluation set comprising 1,043 auscultation examinations (9,319 recordings) was collected from 899 patients. Examinations were assigned to one of four groups: asthma with and without abnormal sounds (AA and AN, respectively), no-asthma with and without abnormal sounds (NA and NN, respectively). Presence of abnormal sounds was evaluated by a panel of 3 physicians that were blinded to the AI predictions. AI was trained on an independent set of 9,847 recordings to determine intensity scores (indexes) of wheezes, rhonchi, fine and coarse crackles and their combinations: continuous phenomena (wheezes + rhonchi) and all phenomena. The pair-comparison of groups of examinations based on Area Under ROC-Curve (AUC) was used to evaluate the performance of each index in discrimination between groups.Results: Best performance in separation between AA and AN was observed with Continuous Phenomena Index (AUC 0.94) while for NN and NA. All Phenomena Index (AUC 0.91) showed the best performance. AA showed slightly higher prevalence of wheezes compared to NA.Conclusions: The results showed a high efficiency of the AI to discriminate between the asthma patients with normal and abnormal sounds, thus this approach has a great potential and can be used to monitor asthma symptoms at home.

A New Three-Phase Smart Meter for Cloud Connection: Network Architecture and Performances

Efficient energy consumption is essential for the development of a smart grid in the power system. As a response, reliable monitoring and management of energy usage is a main priority for the smart grid. The existing energy meter system has a host of concerns, one of which is the lack of full-duplex transmission. To solve this problem, in this paper, we present a novel architecture for a cloud-connected Smart Energy Meter. The proposed meter is capable of real-time measurement of the energy network status and data transfer to a cloud architecture. The meter is suitable for single and polyphase measurement. Its performances are validated on a test field performed on the island of Lipari (Italy).

Generation of hydroxyl radical-activatable ratiometric near-infrared bimodal probes for early monitoring of tumor response to therapy

Tumor response to radiotherapy or ferroptosis is closely related to hydroxyl radical (•OH) production. Noninvasive imaging of •OH fluctuation in tumors can allow early monitoring of response to therapy, but is challenging. Here, we report the optimization of a diene electrochromic material (1-Br-Et) as a •OH-responsive chromophore, and use it to develop a near-infrared ratiometric fluorescent and photoacoustic (FL/PA) bimodal probe for in vivo imaging of •OH. The probe displays a large FL ratio between 780 and 1113 nm (FL780/FL1113), but a small PA ratio between 755 and 905 nm (PA755/PA905). Oxidation of 1-Br-Et by •OH decreases the FL780/FL1113 while concurrently increasing the PA755/PA905, allowing the reliable monitoring of •OH production in tumors undergoing erastin-induced ferroptosis or radiotherapy.

Technology development and commercial applications of industrial fault diagnosis system: a review

Machinery will fail due to complex and tough working conditions. It is necessary to apply reliable monitoring technology to ensure their safe operation. Condition-based maintenance (CBM) has attracted significant interest from the research community in recent years. This paper provides a review on CBM of industrial machineries. Firstly, the development of fault diagnosis systems is introduced systematically. Then, the main types of data in the field of the fault diagnosis are summarized. After that, the commonly used techniques for the signal processing, fault diagnosis, and remaining useful life (RUL) prediction are discussed, and the advantages and disadvantages of these existing techniques are explored for some specific applications. Typical fault diagnosis products developed by corporations and universities are surveyed. Lastly, discussions on current developing situation and possible future trends are in the CBM performed.

Convolutional Neural Network for Wind Turbine Failure Classification Based on SCADA Data

As a renewable energy source and an alternative to fossil fuels, the wind power industry is growing rapidly. However, due to harsh weather conditions, wind turbines (WT) still face many failures that raise the price of energy produced and reduce the reliability of wind energy. Hence, the use of reliable monitoring and diagnostic systems of WTs is of great importance. Operation and maintenance expenses represent 30% of the total cost of large wind farms. The installation of offshore and remote wind farms has increased the need for efficient fault detection and condition monitoring systems. In this work, without using specific custom devices for monitoring conditions, but only increasing the sampling frequency in the sensors already available (in all commercial WT) of the supervisory control and data acquisition system (SCADA), datadriven multiple fault detection is performed, and a classification strategy is developed. The data is processed, and subsequently, using a convolutional neural network (CNN), six faults are classified and evaluated with different metrics. Finally, it should be noted that the classification speed allows the implementation of this strategy to monitor conditions online in real under-production WTs.