The adaptive implementation of the Monte Carlo Method (AMCM) has been applied to optimize the calibration uncertainty of a positive displacement meter against a standard tank in an ISO 17025 accredited volume laboratory. These meters are used for custody transfer in liquid hydrocarbon logistics where any reduction in the uncertainty estimation may have an important economic impact. Several innovations are proposed when applying AMCM; regulation of AMCM convergence by applying the Student's t-distribution, validation of repeatability and filtering of outliers by performing 50 iterations of the AMCM, and characterization of the measurand's Probability Density Function (PDF) which results in a Flatten Gaussian. This work proves that GUM method does not assign the correct PDF. The uncertainty estimation is reduced 7.1 % compared to GUM method.

A field experiment was conducted during Kharif 2018, laid out in Randomized Block Design with three replications having seven treatments viz. N omission (T1), N applied as basal and AT (T2), N as basal, AT and PI (T3), N as basal and top dressing at NDVI threshold of 0.75 (T4), at NDVI threshold of 0.8 (T5), at SPAD threshold of 35.0 (T6) and SPAD threshold of 37.5 (T7) with Rice variety Sahabhagidhan.The study revealed that application of 30 kg N/ha as basal dose and top dressing of 20 kg N/ha twice at 35 and 63 DAS guided by NDVI threshold value of 0.8 (T5) was found to be superior over other treatments with respect to productivity. T5 recorded highest grain yield of 4438 kg/ha which was 17.0% higher than that top dressed at NDVI threshold of 0.75 (T4) and 7.1% higher than that top dressed at SPAD threshold value of 37.5 (T7). In case of SPAD meter, nitrogen top dressed at threshold value of 37.5 (T7) produced grain yield of 4143 kg/ha which was 15.0% higher than T6. T5 produced maximum dry matter of 8678 kg/ha with highest grain yield (4438 kg/ha), straw yield (5092 kg/ha) and harvest index 46.0%.

The VUCA world is the epitome of the challenges linked with some of the undercurrents currently shaping businesses in the projectified society. The terms volatility, uncertainty, complexity and ambiguity (VUCA) are used as descriptive for the continuous flux project managers and project planners are forced to keep at bay. In this article, the authors present an idea to connect the VUCA concept as a risk identification platform to access, identify and isolate low-probability but high-impact events often called black swans or fat tail events. It is argued that the assumptions of conventional risk assessment in the domain of project management can lead to skewed general outcomes due to limitations of the intellect to assess probability. The outline of the VUCA meter is drafted and argued that the meter can augment the conventional risk assessment

Distributed Energy Resources might have a severe influence on Power Line Communications, as they can generate interfering signals and high frequency emissions or supraharmonics that may cause loss of metering and control data. In this paper, the influence of various energy resources on Narrowband Power Line Communications is described and analyzed through several test measurements performed in a real microgrid. Accordingly, the paper describes the effects on smart metering communications through MAC layer analysis. Results show that the commutation frequency of inverters and the presence of battery chargers are remarkable sources of disturbance in low voltage distribution networks. In this sense, the results presented can contribute to efforts towards standardization and normative of emissions at higher frequencies higher, such as CENELEC EN 50160 and IEC/TS 62749.

Ensuring the quality of printed circuit boards (PCBs) in gas meters is essential to the reliable operation and safety of these devices. Traditional methods for detecting welding defects on PCBs, such as machine vision and deep learning, present significant limitations. Machine vision-based approaches lack the precision required in complex environments, while deep learning methods demand large, labeled datasets that are costly and impractical in industrial contexts. This paper introduces a novel melding defect detection method based on discrete optimal transport (DOT) theory, focused on identifying discrepancies in welding areas between sample and reference images. By limiting analysis to the welding regions, the proposed approach reduces computational costs associated with high-resolution image processing while maintaining detection precision and independence from large datasets. The DOT model calculates the optimal mass flow between images, pinpointing specific defects and providing actionable insights for defect tracing and repair, thereby overcoming the limitations of existing methods. Experimental results demonstrate that this approach is both effective and efficient, offering a robust solution for quality assurance in PCB manufacturing for gas meters.

Background: Asthma is ranked 16th among the leading causes of years lived with disability and 28th among the leading causes of disease in the world. Metered dose inhaler remains the principal route for drug administration and it has greater advantages over systemic treatment. In routine use, however, a vast majority of patients make inhalation errors. Suboptimal inhaler technique worsens health outcomes, with poor disease control, and increases the risk of hospitalization. This study aimed to assess practice metered-dose inhalation techniques and associated factors among asthmatic patients at Debre Markos comprehensive specialized hospital, East Gojjam, Amhara region, Ethiopia. Methods: Prospective cross-sectional study was conducted from July 15 to August 30/ 2020. Data were collected through a semi-structured questioner. The data was analyzed using SPSS version 25. Associations between dependent and independent variables were assessed by using binary and multiple logistic regressions. P-values less than 0.05 were considered to be statistically significant in all cases. Results presented in tables, figures, numbers, and percentages. Result: A total of 166 patients had involved in the study, of which 52.4% were females. One hundred eleven (66.9%) of participants had good knowledge of asthma and inhalational techniques with the rest of them had poor knowledge. One hundred eight (65.1%) patients had effective practice on metered-dose inhaler use techniques. Participant's knowledge regarding asthma and meter dose inhaler and marital status has a significant association with their practice of metered-dose inhaler techniques with P-value 0.001 and 0.006 respectively. Conclusion: In this study, most participants are suffering from asthma for a long duration and they have repeated exacerbation. Around two-thirds of patients had good knowledge regarding asthma and meter dose inhaler and practice on metered-dose inhaler techniques. Participants who had poor knowledge and who were unmarried had poor practice on tech metered-dose inhaler techniques. Health education and counseling services should be consistently provided to the clients regarding the proper steps of inhalers use.

Electric torque wrenches play a crucial role in the assembly of high-strength bolts. This study focuses on three assembly processes for high-strength bolts and introduces a statistical analysis method to assess the quality of the bolt assembly. To enhance the performance, a novel, fully digital, Internet of Things (IoT) electric torque wrench is designed, incorporating a smart meter chip. This wrench achieves integration between the current sensor control method and the torque sensor control method using a simplified hardware circuit, ensuring high precision and stability. Additionally, this study employs the AC voltage self-stabilization method and Savitzky–Golay filtering method to enhance the accuracy and robustness of the wrench. Simulation experiments using Simulink and Matlab validate the effectiveness of these two methods. Furthermore, a cloud platform is utilized to enable rapid IoT access for the wrench, facilitating seamless connectivity and data transmission.

The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demand new proposals and opportunities to obtain a domestic energy management system (HEMS), which allows reducing the use of electrical energy. The HEMS incorporates artificial intelligence (AI) techniques to respond to energy demand (DR), which can control, switch, turn on and off, modifying the consumption profile, reducing monthly billing, which as a consequence brings improvement. of the quality of life at home. Based on what has been described, a model for identifying electrical appliances and determining consumption patterns for the home is proposed, using intrusive measurement/actuator equipment called Smart Socket. The Smart Socket measures the electrical variables of voltage and current in real time, calculating the powers and active-reactive-apparent energies of each household appliance (or the most relevant ones). The information provided by the Smart Socket is used as a basis to configure the HEMS, with the aim of optimizing energy use using artificial intelligence techniques and tools, thus reducing energy consumption, CO2 emissions and the user's monthly billing. residential.

In the context of evaluating physical function in individuals with stroke, the 3-meter backward walk test (3MBWT) emerges as a potential tool of interest. The purpose of this study was to assess the test-retest reliability and construct validity of the 3MBWT and its correlation with falling incidents. Conducted in a neurological rehabilitation center, 35 ambulatory individuals with stroke were enrolled within a month post-stroke onset. These participants, with a Functional Ambulation Category score of ≥4, underwent the 3MBWT, Functional Gait Assessment (FGA), 10-Meter Walk Test (10MWT), and 6-Minute Walk Test (6MWT) three times by different physiotherapists. Results indicated the 3MBWT displayed high reliability (r=0.97-0.98) and showed significant construct validity with other established walking tests like the 6MWT (r=-0.78) and 10MWT (r=0.71), with a moderate correlation to the FGA (r=-0.54). No marked differences in test outcomes were observed between participants based on their fall history. Conclusively, the 3MBWT proves to be highly reliable and validates well with existing walking function assessments for stroke patients, suggesting its potential as a time-efficient alternative.

There is a substantial lack of data relating to the hygrothermal traits of existing buildings and building materials. Moisture analysis in building envelopes is 1D (one-dimensional), and 2D and 3D effects and feedback can be neglected. Many European standards treat a moisture percentage in building envelopes as surface and interstitial moisture condensation, such as BS EN ISO 10211, BS EN ISO 13788, BS EN ISO 15148, and BS EN 15026. In this manuscript, there was a tendency to capture a relative quantity of superficial moisture, by metering spots on the inner and outer surface of wall lining systems with a moisture meter. The metering specimens were the buildings with different envelopes: a wooden hut-house, a masonry building and a building partially sheeted in stone. The referent heights on wall linings, for taking the moisture data were specified as 50 cm for a wooden and masonry building and 80 cm for a stone-sheeted building, from the ground. The obtained numeric results were palpable with no regard to the current state of the weather outside. The listed conclusions highlight the significance of a well-tailored building envelope, and its dependence on weather, junctions, gaskets, appropriate materials, etc.

Developing a low-cost wireless energy meter with power quality measurement for smart grid applications represents a significant advancement in efficient and accurate electrical energy monitoring.In increasingly complex and interconnected electrical systems, this device is essential in various applications, such as smart grids, enabling real-time energy usage monitoring. In such a context, smart meters expand the possibilities for sustainable and efficient energy use and promote better utilization of energy sources, especially non-renewable ones. According to the 2020 International Energy Agency (IEA) research, non-renewable energy sources represent 65% of the global total. The smart meter developed in this work is built based on the ESP32 microcontroller and easily accessible components, using a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of the smart meter development was to enhance the software and simplify the hardware. Unlike traditional meters that perform electrical parameter calculations using complex hardware circuits, this project performed the calculations directly on the microcontroller. This approach reduced the complexity of the hardware, simplifying the meter’s design. With the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters were achieved without the need for additional circuits. This software-driven approach with simplified hardware brought benefits such as reduced production costs, lower energy consumption, and increased meter development and updates flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the transmission of collected data to remote monitoring systems for later analysis. The developed smart meter offers cost, energy efficiency, and flexibility benefits. It represents an innovative, efficient, and versatile solution for smart grid applications.

The general circuit topology and principles of low-noise high-voltage power supply are investigated to meet the requirements of the high-voltage bias application in air kerma meters. Two topologies, flyback converter and Royer converter are simulated using SPICE simulation program. The simulation results indicate that the circuit structure of the Royer converter is more complex, but it obtains lower output high voltage noise. While we develop an adjustable high-voltage power supply according to the circuit structure of the Royer converter, and tested it to ensure the design requirements for continuously adjustable output high-voltage linearity. The test results show that the linear adjustment rate is not more than ±0.0025%, the load regulation rate is less than ±0.1%, and the output ripple noise voltage percentage is less than 0.01%. These tested performance make it more suitable for accurate nuclear measurements.

Noise pollution is an environmental problem due to its effect on hearing and other related health issues. The elevated noise levels above standard limits cause hearing loss and other attendant problems. In Nigeria, viewing centers where football matches are watched via satellite are known as ‘Mini Stadium’. In this study, the noise pollution of a viewing center was assessed. To do this, five top teams each from the English Premier League (EPL), La Liga, Bundesliga, League 1, and Serie A were selected for the assessment. A sound level meter (GB: 2266204, made in China) was used for the monitoring. The range of noise levels in dBA showed as follow: EPL (56-108); La Liga (46-106); Bundesliga (54-102); Serie A (49-101); and Ligue 1 (54-101). The results when compared with the standard limits, it was observed that part of the results exceeded the limits, this means that the viewers and others within the viewing center may be prone to the hearing problem. It is recommended that acoustic insulators and hearing aids should be used to reduce the attendant problems associated with noise.

A simple and sensitive method for Cu²⁺ detection was developed using the Cu⁺-catalyzed alkyne-azide cycloaddition reaction, Fe3O4 magnetic nanoparticles (MNPs) as the reaction platform, and a portable blood glucose meter (PGM) as the detection method. Gold nanoparticles (AuNPs) were labeled with glucose oxidase (GOx) and alkyne-functionalized and terminally thiolated ssDNA (C1). In the presence of Cu²⁺ and ascorbate, the functionalized AuNPs were captured onto MNPs modified with azide-functionalized ssDNA (C2) via the Cu⁺-catalyzed alkyne-azide cycloaddition reaction. GOx on the AuNPs surface oxidized glucose (Glu) into gluconic acid and H2O2, reducing the Glu content in the reaction solution, which was quantitatively detected by the PGM. Under optimal conditions, the PGM response of the system showed a good linear relationship with the logarithm of Cu²⁺ concentration in the range of 0.05 to 10.00 μmol/L, with a detection limit of 0.03 μmol/L (3σ). In actual tap water samples, the spiked recovery rate of Cu²⁺ was between 92.30% and 113.33%, and the relative standard deviation was between 0.14% and 0.34%, meeting the detection requirements for Cu²⁺ in real water samples.

In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study aimed to investigate whether it is possible to calculate the time constant of the semiconductor survey meter using the X-ray equipment installed in each medical facility and whether it is possible to calibrate the semiconductor survey meter. Attach an additional filter to the medical X-ray system to satisfy the standards of N-60 to N-120, add more copper plates from there, and calculate the first and second half-value layers to compare with N-60 to N-120 values. Next, we will measure the leakage dose using a medical X-ray system and calculate the time constant of the survey meter. The survey meter is then calibrated and compared with the calibration factors in industrial X-ray system. Per experimental results, it is possible to reproduce the N-60 to N-120 radiation quality using a medical X-ray system and to calculate the time constant from the measured results assuming actual leakage dosimetry using that radiation quality. We also found that the calibration factor was equivalent to that of an industrial X-ray device. It was revealed that semiconductor survey meters can be calibrated using a medical X-ray system.

This study introduces a low-cost solution provided by a specialized source measure unit (SMU), offering an alternative to conventional source meter units. Through precise IV curve measurements for graphene-channel FETs, the SMU demonstrates its capability in accurately characterizing these devices with minimal noise and high accuracy. This cost-effective solution presents a promising avenue for researchers and developers seeking reliable tools for sensor development and characterization, providing valuable insights into the behavior of graphene-channel FETs and the effect of surface illumination without the need for expensive equipment. Additionally, the SMU was validated with known passive and active components, along with probe station integration for microscale connection, ensuring its effectiveness in capturing accurate measurements. The SMU’s focus on collecting IV curves, coupled with its ability to identify device defects, such as Schottky junctions, contributes to its utility in quality testing for semiconductor devices. Its low-cost nature makes it accessible for various research endeavors, enabling efficient data collection and analysis for graphene-based sensor applications.

Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

Low-frequency electromagnetic induction (EMI) is a non-invasive geophysical method that is based on the induction of electromagnetic (EM) waves into the subsurface to quantify changes in electrical conductivity. In this study we present an open (design details and software are accessible) and modular system for the collection of EMI data. The instrument proposed allows to adjust the separations between the transmitter and up to four receiving antennas as well as the acquisition frequency (in the range between 3 and 50 kHz) to permit measurements with variable depth of investigation. The sensor provides access to raw data and the software described in this study allows control of the signal processing chain. The design specifications permit apparent conductivity measurements in the range of between 1 mS/m and 1000 mS/m, with a resolution of 1.0 mS/m and with a sampling rate of up to 10 samples per second. The sensor allows for a synchronous acquisition of a time stamp and a location stamp for each data sample. The sensor has a mass of less than 5 kg, is portable and suitable for 1-person operation, provides 4 hours of operation time on one battery charge, and provides sufficient rigidity for practical field operations.

Life in the modern century is heavily reliant on an enormous amount of electricity consumption as technology has become the most integral part of daily life. In this context, smart grid systems play a pivotal role to maintain the uninterrupted power supply which needs to be monitored in a timely fashion to keep track of the electric consumers’ usage pattern. The smart meter is the one of smart applications of the smart grid that collects huge amounts of consumer load data on a daily basis which has become a focus for various researchers and analyzers to study load characterization. In this paper, an approach has been proposed to recognize the energy consumption patterns among diverse types of consumers ranging from residential to industrial levels. This approach is worth considering not only for load pattern recognition but also for involving customers in different events such as demand response or peak shaving. In such a way, this analytical mechanism certainly assists in reducing power wastage and saving costs. The proposed methodology is based on a two-fold clustering algorithm with the use of state-of-the-art technology, machine learning. The primary goal is to classify electric customers' data collected from smart meters. Then, analyzing the classified results with an aim to predict power consumption patterns for the customers in the future and making the right energy policy that will benefit both the grid operator and consumers as well.

The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes was collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data was collected, the variables available and the representativeness of the sample compared to national estimates. It is intended as a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it.

Abstract: Background: The diagnosis of nasal airway disorders relies on questionnaires, subjective data, and objective data. Interpretation of objective data obtained from measurement technology is not well understood by providers and researchers who use the equipment to test nasal airway function, justifying the need to cre-ate a new clinical tool. The Davidson Airway Function & Nasal Evaluation (DAFNE) Scoring System is a web-based scoring system that allows healthcare providers (MDs, PAs, PTs, APRNs, DMDs, DDSs, SLPs, and DCs) and researchers who treat nasal obstruction and breathing disorders to make more accurate diagnostic decisions, select the appropriate treatment for individualized patient care, monitor treatment progression and outcomes, and show how to approach a collaborative care model with other healthcare professionals. The current technologies, such as 4-phase rhinomanometry, use a simple approach that relies on a measurement tracing intersecting a color-coded y-axis on a four-quadrant graph with diagnostic numeric data output de-scribing nasal airflow and resistance. This is a good source of biofeedback demonstrating abnormal nasal breathing patterns but lacks interpretation guidelines to describe the severity of disease. The more sophisti-cated proprietary DAFNE approach uses multiple parameters which include airflow restriction, speed, vol-ume, and patient characteristics to identify patients’ disease progression much more accurately. Additionally, it explains how to segregate patients into appropriate treatment options which minimizes risks and compli-cations associated with sleep and breathing disorders while improving patient outcomes. Methods: Electronic searches of PubMed, MEDLINE, EMBASE Cochrane Library, and Scopus of publications between 1988-2022 were necessary to identify studies which determined the ranges of nasal function measurement parameters among three airway measurement technologies used in writing the DAFNE Score algorithm. The performance of DAFNE was evaluated compared to the research data and patient demographics used in previous studies. Results: By using primary outcome measurements from the literature, we were able to create a reliable, re-peatable, and validated tool to facilitate patient identification, treatment stratification, and collaboration for sleep and nasal breathing dysfunction. Subsequently, we found DAFNE could be used to identify the appro-priateness of a sleep study. Conclusions: DAFNE Scoring System has demonstrated the ability to detect high risk patients in the pediatric and adult populations susceptible of sleep apnea and breathing dysfunction needing a sleep study and treatment to improve nasal airway function and sleep. DAFNE Scoring System should be used as an adjunct tool in routine clinical practice and research to further understand the technol-ogy data output, identify patients at risk, stratify treatment options, progress, and stabilization after treatment, and how to collaborate with other healthcare providers to improve patient outcomes.

Background: The Davidson Airway Function & Nasal Evaluation (DAFNE) Scoring System was developed as an intuitive and research-based scoring system that could be validated through beta testing and easily introduced to healthcare providers of several subspecialties who treat nasal obstruction and breathing disorders (MDs, PAs, PTs, APRNs, DDSs, and DCs). This scoring system was shown to increase the knowledge of airway function, nasal measurement parameters, and identification of proper treatment options for sleep and breathing disorders. The basis for the DAFNE score was developed from a systematic review of nasal measurement data. Methods: Electronic searches of PubMed, MEDLINE, EMBASE Cochrane Library, and Scopus of publications between 1988-2022 were used to identify studies validating nasal function measurement parameters to create the algorithm for the DAFNE Score™. The systematic review was accomplished using the 2020 ‘Preferred Reporting Items for Systematic Reviews’ (PRISMA) guidelines. Results: Twenty studies met the inclusion criteria for systematic review. Primary outcomes measurements demonstrated reliability, repeatability and validity of the DAFNE measurement technologies, data and output. Conclusions: The data analysis and systematic review uncovered a need and framework to develop and validate a web-based software algorithm for global access to improve the understanding of data interpretation of nasal measurements from three nasal measurement technologies. DAFNE Scoring should be used as an adjunct tool in routine clinical practice and research to further understand the technology data output and how to collaborate with other healthcare providers to improve patient outcomes.

In the dynamic landscape of 6G and smart cities, Visible Light Communication (VLC) assumes critical significance for IoT (Internet of Things) applications spanning diverse sectors. The escalating demand for bandwidth and data underscores the need for innovative solutions, positioning VLC as a complementary technology within the electromagnetic spectrum. This paper focuses on the relevance of VLC in the 6G paradigm, shedding light on its applicability across smart cities and industries. The paper highlights the growing efficiency of lighting LEDs in infrastructure, facilitating the seamless integration of VLC. The study then emphasizes VLC’s robustness in outdoor settings, demonstrating effective communication up to 10 meters. This resilience positions VLC as a key player in addressing the very last meter of wireless communication, offering a seamless solution for IoT connectivity. By introducing an a freely available open-source simulator combined to an alternative waveform, UFMC, the study empowers researchers to dimension applications effectively, showcasing VLC’s potential to improve wireless communication in the evolving landscape of 6G and smart cities.

Heat metres are used to calculate the consumed energy in central heating systems. The subject of this article is to prepare a method of predicting a failure of a heat meter in the next settlement period. Predicting failures is essential to coordinate the process of exchanging the heat metres and to avoid inaccurate readings, incorrect billing and additional costs. The reliability analysis of heat metres was based on historical data collected over many years. Three independent models of machine learning were proposed, and they were applied to predict failures of metres. The efficiency of the models was confirmed and compared using the selected metrics. The optimisation of hyperparameters characteristics for each of models was successfully applied. The article shows that the diagnostics of devices does not have to rely only on newly collected information, but it is also possible to use the existing big data sets.

The need to increase the energy efficiency of buildings as well as the use of local renewable heat sources has caused that heat meters are used not only to calculate the consumed energy but also for the active management of central heating systems. Increasing the reading frequency and the use of measurement data to control the heating system expands the requirements for the reliability of heat meters. The aim of the research is to analyse a large set of meters in the real network and predict their faults to avoid inaccurate readings, incorrect billing, heating system disruption and unnecessary maintenance. The reliability analysis of heat metres, based on historical data collected over several years, shows some regularities which cannot be easily described by physics-based models. The failure rate is almost constant and does depend on the past but is a non-linear combination of state variables. To predict meters' failures in the next settlement period, three independent machine learning models are implemented and compared with selected metrics because even the high performance of a single model (87\% True Positive for Neural Network) may be insufficient to make a maintenance decision. Additionally, performing hyperparameters optimisation boosts models' performance by a few percent. Finally, three improved models are used to build an ensemble classifier which outperforms the individual models. The proposed procedure ensures the high efficiency of fault detection (>95\%), while maintaining overfitting at the minimum level. The methodology is universal and can be utilised to study the reliability and predict faults of other types of meters and different objects with the constant failure rate.

Radio bursts provide important diagnostics of energetic phenomena of the Sun. In particular, bursts in decimetric and metric wavelengths probe the physical conditions and the energy release processes in the low corona as well as their association with heliospheric phenomena. The advent of spectral radio data with high time and high frequency resolution has provided a wealth of information on phenomena of short duration and narrow bandwidth. Of particular value are spectral data combined with imaging observations at specific frequencies. In this work we briefly review the results of a series of observations comprised from high-sensitivity, low-noise dynamic spectra obtained with the acousto-optic analyzer (SAO) of the ARTEMIS-IV/JLS solar radiospectrograph, in conjunction with high time-resolution images from the Nançay Radioheliograph (NRH). Our studies include fine structures embedded in type IV burst continua (mostly narrow-band "spikes" and intermediate drift "fiber" bursts) and spike-like structures detected near the front of type-II bursts. The implications of the observational results to theoretical models are discussed.

This article seeks to strengthen efforts in technological developments based on the classification of PET plastic that positively impacts sustainable development and contributes to suitable solutions in collection centers in Mexico. Three experiment designs and machine learning tools for data processing were developed. For the experimentation, three factors were considered: the bottle's size, the amount of liquids inside the bottle, and the label on the bottle. The first experiment was to identify the distance of the sensor from post-consumer PET bottles. The second experiment was to determine the detection ability of the sensor with different levels of liquids inside the bottles, and the third was to determine the detection ability on the bottle labels. A digital lux meter on a microcontroller was developed to monitor illuminance on post-consumer PET plastic when it contains liquid as it passes through a conveyor belt for processing at an average rate of three bottles per second. With the implemented methodology, liquids were satisfactorily detected inside the transparent PET bottles when they had beverages between 25% and 100% of their capacity. Finally, this paper highlights that it is possible to implement an affordable design to identify bottles with liquids for collection centers.