Monitoring Purposes Research Articles

Evaluation of neurofilament light chain as a biomarker in dogs with structural and idiopathic epilepsy.

Neurofilament light chain (NfL) is a frequently used biomarker in humans for both diagnostic and therapeutic monitoring purposes in various neurologic diseases. It was hypothesized that dogs with diagnosed structural epilepsy (SE) would have a significantly higher serum NfL concentrations compared to dogs with idiopathic epilepsy (IE). The secondary hypothesis was that dogs would have a significantly higher serum NfL concentrations when measured within 7 days after a seizure compared to being seizure-free for at least 30 days. Fifty client-owned dogs presented to the neurology service for evaluation of seizures were enrolled. Fourteen dogs had SE and 36 dogs had IE. Prospective cohort study performed on 52 serum samples obtained for NfL concentration measurement using single molecule array technology. The median serum concentration of NfL in dogs with SE was significantly higher (109 pg/mL; range, 11.4-741.3 pg/mL) than in dogs with IE (17.7 pg/mL; range, 5.8-188 pg/mL; Wilcoxon rank sum test, P = .001). No significant relationship was found between serum NfL concentration and time of sampling in relation to the most recent seizure in dogs with IE. Serum NfL may serve as an adjunctive biomarker for the differentiation of SE and IE.

Rational Design of a Portable Chemometric-Assisted Voltammetric Sensor Based on Ion-Imprinted Polymeric Film for Co(II) Determination in Water.

Herein, chemometric-assisted synthesis of electrochemical sensors based on electropolymerised ion-imprinted polymeric (e-IIP) films was explored. Co(II)-IIPs sensors were prepared by performing electropolymerisation procedures of polymerisation mixtures comprising varying concentrations of an electroactive o-aminophenol (o-AP) monomer and Co(II) ions, respectively, according to the Taguchi L9 experimental design, exploiting the simultaneous evaluation of other controlled parameters during electrosynthesis. Each e-IIP developed from Taguchi runs was compared with the respective non-imprinted polymer (NIP) films and fitted according to Langmuir-Freudlich isotherms. Distinctive patterns of low and high-affinity films were screened based on the qualities and properties of the developed IIPs in terms of binding kinetics (KD), imprinting factor, and the heterogeneity index of produced cavities. These results can provide a generic protocol for chemometric-assisted synthesis of e-IIPs based on poly-o-AP, providing highly stable, reproducible, and high-affinity imprinted polymeric films for monitoring purposes.

Dynamics of changes in the temperature coefficient of electrical conductivity of distilled water in conductometric cells during heating and cooling

The problem of taking into account the influence of air on the properties of distilled water is considered, namely the lack of a single generally accepted method for calculating such an influence. The sensitivity of the structure of water to the influence of external factors is described and the possibility of recording and studying such factors by changes in the temperature coefficient of electrical conductivity of water is shown. The dependences of the temperature coefficient of electrical conductivity of distilled water on the rate of change in water temperature, the degree of filling of conductometric cells, as well as on the intensity of the exchange of carbon dioxide between water and air across their interface have been studied. It is noted that these metabolic processes are currently insufficiently studied. A hardware-software measuring complex has been developed and manufactured to study the temperature coefficient of electrical conductivity of water when its temperature changes within the range of 20–55 °C. The temperature coefficient of electrical conductivity of water in sealed conductometric cells was measured at different degrees of filling the cells with distilled water and the rate of heating and cooling of water. The degree of filling of the cells varied within the range of 10–100 %, the rate of change in water temperature varied within the range of 0.04–2.00 °C/min. With a constant heating and cooling time of 15 minutes in all experiments, the change in speed was achieved by changing the temperature of the heating element. The integral temperature coefficient of electrical conductivity is calculated based on the initial and final values of electrical conductivity and water temperature in each measurement cycle. The dependences of the temperature coefficient of electrical conductivity on the rate of change in water temperature at several constant degrees of cell filling were obtained. It has been shown that with a constant ratio of the volumes of water and air in the cell and an increase in the rate of heating of water, the temperature coefficient of electrical conductivity of water decreases by 19–22 %. It has been established that at a constant rate of water heating, with a decrease in the volume of water in the cell, the temperature coefficient of electrical conductivity of water decreases by 40–42 %. The results obtained can be used to quantify the dissociation coefficient of carbonic acid, the mobility of hydrogen ions, as well as the intensity of the gas exchange process under various external influences on water. Refinement of data on the electrical conductive properties of water and processes at the water/air interface is necessary for the development of models of atmospheric phenomena and climate change, as well as for the creation of sensors for weak changes in environmental parameters for the purposes of both environmental monitoring and medical diagnostics.

Feasibility of reservoir monitoring in the Groningen gas field using ghost reflections from seismic interferometry

SUMMARY Seismic interferometry (SI) retrieves new seismic responses, for example reflections, between either receivers or sources. When SI is applied to a reflection survey with active sources and receivers at the surface, non-physical (ghost) reflections are retrieved as well. Ghost reflections, retrieved from the correlation of two primary reflections or multiples from two different depth levels, are only sensitive to the properties in the layer that cause them to appear in the result of SI, such as velocity, density and thickness. We aim to use these ghost reflections for monitoring subsurface changes, to address challenges associated with detecting and isolating changes within the target layer in monitoring. We focus on the feasibility of monitoring pore-pressure changes in the Groningen gas field in the Netherlands using ghost reflections. To achieve this, we utilize numerical modelling to simulate scalar reflection data, deploying sources and receivers at the surface. To build up subsurface models for monitoring purposes, we perform an ultrasonic transmission laboratory experiment to measure S-wave velocities at different pore pressures. Applying SI by autocorrelation to the modelled data sets, we retrieve zero-offset ghost reflections. Using a correlation operator, we determine time differences between a baseline survey and monitoring surveys. To enhance the ability to detect small changes, we propose subsampling the ghost reflections before the correlation operator and using only virtual sources with a complete illumination of receivers. We demonstrate that the retrieved time differences between the ghost reflections exhibit variations corresponding to velocity changes inside the reservoir. This highlights the potential of ghost reflections as valuable indicators for monitoring even small changes. We also investigate the effect of the sources and receivers’ geometry and spacing and the number of virtual sources and receivers in retrieving ghost reflections with high interpretability resolution.

Investigations on the enhanced anti-microbial activity of one step synthesized ZnO, WO3, and rGO nano particles and fabrication of rGO nano electrode for EMG biomedical application

Abstract The proposed study aims to synthesize ZnO, WO3, and rGO nano particles (NPs) using simple and effective chemical reduction technique and to investigate its biomedical applications. According to the XRD results, synthesized NPs had hexagonal, monoclinic, and cubic crystal forms. The FESEM micro image shows sheet-like structure for rGO NPs, an algal crystal structure for ZnO, and a bulk cluster formation for WO3 NPs. The value of band gap was found to be 5.75, 5.37 and 3.01 eV for ZnO, WO3, and rGO NPs respectively. The presence of diverse functional groups in the produced NPs was verified using Fourier-transform infrared spectroscopy. Investigations against different microorganisms indicated an augmentation in the antibacterial activity for ZnO and WO3 NPs. Electrodes coated with rGO NP were made and discovered to be effective in collecting human biomedical data more effectively. The performance of rGO NP coated electrodes was compared using a development board for electromyography analysis along with an android application for monitoring purposes. The quality of the signal, the ratio between the signal and the background noise, as well as the level of impedance between the electrode and the skin was observed to be better than conventional electrodes.

Pre-job loss grief reactions and work attachment among sick-listed employees: Introduction of the imminent Job Loss Scale

BackgroundWith this study, we aimed to explore the emotional experiences of sick-listed employees facing imminent job loss, as this emotional distress may hinder successful job search outcomes. The study had two objectives: (1) to develop and validate the Imminent Job Loss Scale (IJLS) for assessing pre-job loss grief reactions and (2) to examine its relationship to work attachment.MethodsDevelopment of the 9-item IJLS was carried out using feedback from an expert panel, consisting of five academic experts in grief and labour, five re-integration specialists, and five sick-listed employees facing imminent job loss. The psychometric properties of the IJLS were evaluated, and its association with work attachment was examined using data from 200 sick-listed employees facing imminent job loss.ResultsThe IJLS demonstrated strong internal consistency and temporal stability, distinctiveness from depression and anxiety symptoms, and solid convergent validity. Work-centrality and organizational commitment were positively related to pre-job loss grief reactions, while work engagement and calling showed no significant associations.ConclusionThis study provides valuable insights into pre-job loss grief reactions and shows the potential utility of the IJLS for screening and monitoring purposes. Understanding pre-job loss grief reactions can improve the re-integration and job prospects of sick-listed employees. In future research, explorations of these dynamics should continue to provide better support to sick-listed employees during this challenging period.

A multidimensional and multi-criteria framework for measuring the circularity of energy generation systems at national level

The circular economy can play a key role in the clean energy transition. In fact, it can enable the shift to renewable power generation addressing the emerging security risks related to supply chains of minerals and metals crucial for green energy technologies. Circularity metrics are essential for assessing the effectiveness of actions taken and the progress toward measurable goals. In this context, through an action research approach the authors developed a framework of indicators to quantify the level of circularity of the whole electricity generation systems at the macro scale. The framework includes indicators related to energy policies, materials, energy, and waste flows and environmental sustainability dimensions and classifies the systems as “beginner”, “started”, “concerned” and “circular”. The framework was applied to the Italian electricity generation system in 2020. The results of the analysis showed that the Italian electricity generation system could be classified as “concerned” in the circular transition. Water circularity, the recycling rate of waste and the usage of minerals and metals, as well as the need to enhance the installation of renewable capacity, resulted in crucial areas for enhancing the circularity of the system. In addition, the application to the case study highlighted the need for the creation of comprehensive databases that increase the accessibility of the data required for measurement and subsequent monitoring purposes. The proposed framework can support the policy makers in planning strategies aimed at increasing the circularity of the electricity generation system at the national level.

Monitoring of cooling tower water pumps using Arduino data acquisition device

Investing equipment to monitor the condition of assets through vibration can be expensive. Recent development of alternative microcontrollers has enabled researchers to study its potential to replace expensive equipment for monitoring purposes. This study aimed to investigate the potential of alternative microcontrollers to be used as devices to monitor the condition of water pumps. An internet of things (IoT) device was developed that can measure vibration and be applied to monitor water pumps. The vibration data was obtained when the pumps were operated and sent to the condition-based monitoring system (CBMS) database via Wi-Fi network. The vibration data was observed, and current and future condition was identified through vibration analysis.

Partial Discharge Monitoring of Power Transformers by Calibrated UHF Measurements

Partial discharge (PD) measurement is an established technique to detect local defects in the oil/paper insulation. They can be measured by electrical PD measurements according to IEC 60270 or by electromagnetic measurements in the ultra-high frequency range (UHF: 300 MHz – 3 GHz). The electromagnetic emissions of PD are recorded using an UHF sensor which is inserted into the transformer tank. The importance of PD measurements is reflected in the standard for charge-based electrical measurements (IEC 60270). Because of the standard, apparent charge, QIEC, became an essential factor for determining insulation quality in transformers. To be an accepted factor for both, quality testing in factory acceptance tests (FAT) or site acceptance tests (SAT) and continuous PD monitoring, the UHF technology must be as reliable and reproducible as the electrical method. To achieve this, a standardized calibration procedure is needed: It makes UHF measurement results from different systems comparable. Because this calibration procedure was lacking in the past, the UHF technology does not provide comparability between measurement systems and different sensors, yet. However, it shows advantages for on-site acceptance tests like SAT, continuous monitoring, and diagnostic purposes. E.g., regarding signal to noise ratios, the possibility for localization of PD sources and also for practical reasons like preparation times and accessibility on-site. This contribution presents a PD monitoring system which can be calibrated according to the calibration process described recently in Cigré TB 861 and two different types of UHF PD sensors for power transformers. One sensor type is for new transformers using dielectric windows and the other is for retrofitting transformers using their DN50/80 drain valves. A recommendation or strategy on where to place window-type UHF sensors at the tank of new power transformers is also provided in accordance with TB 861. Additionally, this contribution provides a description of the calibration process for UHF PD measurement systems consisting of two steps: 1) Calibration of the measurement instrument analogous to the calibration of the electrical PD measurement circuit using a defined reference signal. 2) Calibration of a UHF sensor using its characteristic antenna factor (AF). Furthermore, it shows three case studies from different power transformers equipped with UHF sensors and UHF PD monitoring systems.

Spontaneous, persistent, T cell-dependent IFN-γ release in patients who progress to Long Covid.

After acute infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a proportion of patients experience persistent symptoms beyond 12 weeks, termed Long Covid. Understanding the mechanisms that cause this debilitating disease and identifying biomarkers for diagnostic, therapeutic, and monitoring purposes are urgently required. We detected persistently high levels of interferon-γ (IFN-γ) from peripheral blood mononuclear cells of patients with Long Covid using highly sensitive FluoroSpot assays. This IFN-γ release was seen in the absence of ex vivo peptide stimulation and remains persistently elevated in patients with Long Covid, unlike the resolution seen in patients recovering from acute SARS-CoV-2 infection. The IFN-γ release was CD8+ T cell-mediated and dependent on antigen presentation by CD14+ cells. Longitudinal follow-up of our study cohort showed that symptom improvement and resolution correlated with a decrease in IFN-γ production to baseline levels. Our study highlights a potential mechanism underlying Long Covid, enabling the search for biomarkers and therapeutics in patients with Long Covid.

The implementation of long-term prudent valuation models across the UK and Mainland Europe for financial regulation purposes

ABSTRACT The Basel 3 guidance on bank regulation includes a new definition of real estate valuation which differs from existing bases. Prudently conservative valuation or prudent value includes four criteria which include the exclusion of ‘expectations of price increases’ and it must be ‘adjusted to take into account the potential for the current market price to be significantly above the value that would be sustainable over the life of the loan’. ‘National supervisors should provide guidance’ and ‘If a market value can be determined, the valuation should not be higher than the market value’. Prudent value has synergies with existing post Global Financial Crisis (GFC) debates concerning long-term valuations for commercial real estate bank loan origination and monitoring purposes. This paper outlines the concepts of long-term and prudent values in more detail, reviews the literature on long-term valuation and develops a framework for prudent value. It also puts that literature into a wider perspective on the analysis of market cycles and portfolio disaggregation within performance measurement, and suggests ways in which the property industry could implement the current Basel 3 prudent value proposals.

Characterization of virulence factors and antimicrobial resistance in Staphylococcus spp. isolated from clinical samples.

The virulence factors, antibiotic resistance patterns, and the associated genetic elements have been investigated in Staphylococcus species. A total of 100 strains has been isolated from clinical samples in the Microbiology Laboratory of Hesperia Hospital, Modena, Italy, and identified as Staphylococcus aureus (65), Staphylococcus epidermidis (24), Staphylococcus hominis (3), Staphylococcus saprophyticus (3), and Staphylococcus warneri (5). All the strains were analyzed to determine phenotypic and genotypic characters, notably the virulence factors, the antibiotics susceptibility, and the genetic determinants. The highest percentage of resistance in Staphylococcus spp. was found for erythromycin and benzylpenicillin (87% and 85%, respectively). All S. aureus, two S. epidermidis (8.3%), and one S. saprophyticus (33.3%) strains were resistant to oxacillin. The methicillin resistance gene (mecA) was detected by polymerase chain reaction (PCR) amplification in 65 S. aureus strains and in 3 coagulase-negative staphylococci (CoNS) (8.6%). With regard to the virulence characteristics, all the S. aureus were positive to all virulence tests, except for slime test. Among the CoNS isolates, 19 (79.1%) S. epidermidis and one (33.3%) S. saprophyticus strains resulted positive for the slime test only. The results obtained are useful for a more in-depth understanding of the function and contribution of S. aureus and CoNS antibiotic resistance and virulence factors to staphylococcal infections. In particular, the production of slime is very important for CoNS, a virulence factor frequently found in infections caused by these strains. Further investigations on the genetic relatedness among strains of different sources will be useful for epidemiological and monitoring purposes and will enable us to develop new strategies to counteract the diffusion of methicillin-resistant S. aureus (MRSA) and CoNS strains not only in clinical field, but also in other related environments.

Research on Multi-Point Monitoring Data Grid Model and Inversion Positioning Method for Gas Leakage in Oil and Gas Stations

The safe and stable operation of oil and gas stations makes a positive contribution to the stability and reliability of the natural gas supply. In order to reduce the impact of leakage and diffusion accidents in the station, it is necessary to develop an effective method to monitor and locate the leakage source quickly and accurately. This study proposes a multi-point monitoring data grid model to achieve the full-coverage monitoring of oil and gas stations. In addition, on this basis, a leakage source inversion positioning model is established to realize the leakage positioning of the station. A field experiment was carried out with an oil and gas station as an example. The results show that the optimal layout of points needs to consider the influence of environmental factors, confirmed by computational fluid dynamics (CFD) simulation. The optimized data interpolation not only reduces the cost of the monitoring point layout. In addition, through the comparison of multi-objective optimization algorithms and a robustness test, it can be found that the convergence efficiency and accuracy of the inversion positioning algorithm in this study have been greatly improved. Compared with the manual auxiliary positioning method, this method effectively solves the problem of leakage monitoring and positioning of oil and gas stations, and can achieve the purpose of leakage risk monitoring and “reducing cost and increasing efficiency”.

InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography

BackgroundThis study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (PPG) signals for sleep monitoring with wearable devices. Furthermore, our aim was to develop a more efficient sleep monitoring method by considering both the interpretability and uncertainty of the model’s prediction results, with the goal of providing support to medical professionals in their decision-making process.MethodThe developed 4-class sleep staging model based on continuous PPG data incorporates several key components: a local attention module, an InceptionTime module, a time-distributed dense layer, a temporal convolutional network (TCN), and a 1D convolutional network (CNN). This model prioritizes both interpretability and uncertainty estimation in its prediction results. The local attention module is introduced to provide insights into the impact of each epoch within the continuous PPG data. It achieves this by leveraging the TCN structure. To quantify the uncertainty of prediction results and facilitate selective predictions, an energy score estimation is employed. By enhancing both the performance and interpretability of the model and taking into consideration the reliability of its predictions, we developed the InsightSleepNet for accurate sleep staging.ResultInsightSleepNet was evaluated using three distinct datasets: MESA, CFS, and CAP. Initially, we assessed the model’s classification performance both before and after applying an energy score threshold. We observed a significant improvement in the model’s performance with the implementation of the energy score threshold. On the MESA dataset, prior to applying the energy score threshold, the accuracy was 84.2% with a Cohen’s kappa of 0.742 and weighted F1 score of 0.842. After implementing the energy score threshold, the accuracy increased to a range of 84.8–86.1%, Cohen’s kappa values ranged from 0.75 to 0.78 and weighted F1 scores ranged from 0.848 to 0.861. In the case of the CFS dataset, we also noted enhanced performance. Before the application of the energy score threshold, the accuracy stood at 80.6% with a Cohen’s kappa of 0.72 and weighted F1 score of 0.808. After thresholding, the accuracy improved to a range of 81.9–85.6%, Cohen’s kappa values ranged from 0.74 to 0.79 and weighted F1 scores ranged from 0.821 to 0.857. Similarly, on the CAP dataset, the initial accuracy was 80.6%, accompanied by a Cohen’s kappa of 0.73 and weighted F1 score was 0.805. Following the application of the threshold, the accuracy increased to a range of 81.4–84.3%, Cohen’s kappa values ranged from 0.74 to 0.79 and weighted F1 scores ranged from 0.813 to 0.842. Additionally, by interpreting the model’s predictions, we obtained results indicating a correlation between the peak of the PPG signal and sleep stage classification.ConclusionInsightSleepNet is a 4-class sleep staging model that utilizes continuous PPG data, serves the purpose of continuous sleep monitoring with wearable devices. Beyond its primary function, it might facilitate in-depth sleep analysis by medical professionals and empower them with interpretability for intervention-based predictions. This capability can also support well-informed clinical decision-making, providing valuable insights and serving as a reliable second opinion in medical settings.

Toward a near real-time magma ascent monitoring by combined fluid inclusion barometry and ongoing seismicity.

Fluid inclusion microthermometry on olivines, clinopyroxenes, and amphiboles was used during a volcanic eruption, in combination with real-time seismic data and rapid petrographic observations, for petrological monitoring purposes. By applying this approach to the study of 18 volcanic samples collected during the eruption of Tajogaite volcano on La Palma Island (Canary Islands) in 2021, changes in the magma system were identified over time and space. Magma batches with distinct petrographic and geochemical characteristics emerged from source zones whose depth progressively increased from 27 to 31 kilometers. The rise of magma of deeper origin is attested by fluid inclusions made of N2 and CO, markers of mantle outgassing. Magma accumulation occurred over different durations at depths of 22 to 27 and 4 to 16 kilometers. Time-integrated magma ascent velocities (including ponding times) were estimated at between 0.01 and 0.1 meters per second. This method is cost-effective and quickly identifies changes in the magma system during an eruption, enhancing petrological monitoring procedures.

Machine Learning-Enabled Environmentally Adaptable Skin-Electronic Sensor for Human Gesture Recognition.

Stretchable sensors have been widely investigated and developed for the purpose of human motion detection, touch sensors, and healthcare monitoring, typically converting mechanical/structural deformation into electrical signals. The viscoelastic strain of stretchable materials often results in nonlinear stress-strain characteristics over a broad range of strains, consequently making the stretchable sensors at the body joints less accurate in predicting and recognizing human gestures. Accurate recognition of human gestures can be further deteriorated by environmental changes such as temperature and humidity. Here, we demonstrated an environment-adaptable high stress-strain linearity (up to ε = 150%) and high-durability (>100,000 cycles) stretchable sensor conformally laminated onto the body joints for human gesture recognition. The serpentine configuration of our ionic liquid-based stretchable film enabled us to construct broad data sets of mechanical strain and temperature changes for machine learning-based gesture recognition. Signal recognition and training of distinct strains and environmental stimuli using a machine learning-based algorithm analysis successfully measured and predicted the joint motion in a temperature-changing environment with an accuracy of 92.86% (R-squared). Therefore, we believe that our serpentine-shaped ion gel-based stretchable sensor harmonized with machine-learning analysis will be a significant achievement toward environmentally adaptive and multianalyte sensing applications. Our proposed machine learning-enabled multisensor system may enable the development of future electronic devices such as wearable electronics, soft robotics, electronic skin, and human-machine interaction systems.

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

An accurate petrophysical model of the subsurface is essential for resource development and CO2 sequestration. We present a new workflow that provides a high-resolution estimate of petrophysical reservoir properties using seismic data with rock physics modeling and machine-learning techniques (i.e., deep learning neural networks). First, we compare the sequential prediction of the following petrophysical attributes: mineralogy, porosity, and fluid saturation, with the simultaneous prediction of all of the properties using the Volve field in the Norwegian North Sea as an example. The workflow shows that the sequential prediction produces a more efficient and accurate classification of petrophysical properties (the RMS error between the predicted and the original seismic trace is 50% smaller for the sequential compared to the simultaneous procedure). Next, the seismic amplitude response of the reservoirs was studied using rock physics modeling and amplitude versus offset (AVO) analysis to distinguish the different lithologies and fluid types. To ascertain the optimal hydrocarbon production areas, we performed Bayesian seismic inversion and applied machine learning to estimate the petrophysical properties. We examined how porosity, Vclay, and fluid variations affect the elastic properties. In Poisson’s ratio versus the P-wave impedance domain, a 10% porosity increase decreases the acoustic impedance (AI) by 30%, while a 20% Vclay decrease increases the AI by 12%. The Utsira Formation in the Volve field (5 km north of the Sleipner Øst field) was evaluated as a potential CO2 geological storage unit using Gassmann fluid substitution and seismic modeling. We look to assess the elastic property variation caused by CO2 saturation changes for monitoring purposes and simulate the effect. In the first 10% CO2 substitution, the P-wave velocity decrease is 12%, a subtle effect is observed for higher CO2 saturation values, and S-wave velocity (Vs) increases with CO2 saturation. Our analysis aspires to assist future reservoir studies and CO2 sequestration in similar fields.

A New Method for Detecting Partial Discharge in T-Type Terminals of Power Cable

Breakdown accidents in T-type terminals of power cables occur occasionally and abruptly, causing severe explosions and seriously threatening the reliable operation of the distribution grid. As we know, there haven't been any practical sensors to detect partial discharge online in the T-type terminals with a rated voltage of 10 kV up to now. A novel method for detecting partial discharge (PD) in T-type terminals is proposed in this manuscript. Keeping the original structure of the T-type terminal intact, a sensor is embedded inside the T-type terminal plug to measure the PD induced voltage signal. The equivalent circuit is built. The measurement mechanism of the proposed method is studied. Two kinds of defects are manufactured in the T-type terminals, which are the stress tube displacement and the lack of the stress tube. The discharge tests are carried out and the PD induced voltage pulse signals are detected by the proposed sensor. Although the aim of this manuscript is to validate the proposed method, the characteristics of the PD induced voltages with different defects are compared. It is found that compared with the defect of the lack of the stress tube, the initial discharge voltage is large for the defect of the stress tube displacement. The dominant frequencies and the increased rate of PD induced voltage are very different. The device can be deployed in the cable system to achieve the purpose of online PD monitoring.

Development of an internet of things-based weather station device embedded with O<sub>2</sub>, CO<sub>2</sub>, and CO sensor readings

Weather station devices are used to monitor weather parameter conditions, such as wind direction, speed, rainfall, solar radiation level, temperature, and humidity. This article discusses the design of a customized weather station embedded with gas concentration readings, whereby the gas concentration measurement includes oxygen (O<sub>2</sub>), carbon dioxide (CO<sub>2</sub>), and carbon monoxide (CO). The measurements and data processing of input sensors were transmitted to an Arduino Uno microcontroller, and the input data were then remitted to Wemos D1 Mini to be uploaded to a cloud server. Furthermore, the gas sensors' characterization methods were also considered to reveal the obtained results of accuracy, precision, linearity, and hysteresis. An android-based mobile application was also designed for monitoring purposes. The system in our experiment utilized an internet connection with a field station, base station, and database server.

Determination of controlled parameters for designing a network for geotechnical monitoring of underground pipelines in the cryolithozone

Relevance. Determined by the need for geotechnical monitoring in the areas of distribution of permafrost soils for underground pipelines. The geotechnical system “permafrost soil – underground pipeline” is subject to the influence of exogenous geological processes during operation, cyclical changes in the state of soils of the seasonal layer of thawing and freezing, thawing of permafrost soils from the thermal effect of the pipeline, changing the plan-altitude position. The variety of schemes for interaction of the pipeline body with varieties of lithological structure of permafrost soils along the route should be worked out in detail at the design stage and also assessed in detail during geotechnical monitoring of the pipeline. The lack of regulatory requirements for the monitored parameters of underground pipelines leads to the use of non-standardized and varied monitoring networks from project to project. The lack of standard values ​​for the maximum deformations of an underground pipeline leads to the need to use calculation methods for determining the maximum deformations of the base and pipeline. The authors proposed the use of a comprehensive methodology for calculating underground pipelines, developed by employees of JSC TomskNIPIneft to determine the maximum permissible deformations of the pipeline at each point of the route with the presence of permafrost soils at the design stage, with further use of the results obtained as a controlled criterion when conducting geotechnical monitoring. Aim. To determine the controlled parameters for geotechnical monitoring of underground pipelines in the cryolithozone at the operational stage based on the application of a comprehensive methodology for calculating underground pipelines, as well as the formation of requirements for the system of information points. Methods. Review of the regulatory framework for geotechnical monitoring, analysis and evaluation of the applied methods and designs for monitoring underground pipelines, analysis of a comprehensive methodology for calculating underground pipelines in relation to the purposes of geotechnical monitoring. Results. Based on the results of the review of regulatory and technical documentation, the authors have revealed a lack of specification of methods, equipment, volume of the geotechnical monitoring network and frequency of pipeline monitoring, as well as the requirement for the use of the calculation method of limiting deformations and the lack of description of possible methods. A detailed analysis of the complex methodology for pipeline calculations, developed at JSC TomskNIPIneft, substantiates its use for geotechnical monitoring purposes, and notes the advantage of the methodology in the detail of the obtained limit values ​​of deformation with an accuracy of one meter along the axis of the pipeline route. The authors studied the methods and designs used for monitoring deformations of underground pipelines and established the compatibility of using a complex methodology with any design and method of monitoring. The paper introduces the list of the main monitored parameters and justification for the scope of the geotechnical monitoring network for underground pipelines.