Observing System Research Articles

Computer Vision-Based Research on the Mechanism of Stick–Slip Vibration Suppression and Wear Reduction in Water-Lubricated Rubber Bearing by Surface Texture

Water-lubricated rubber bearings are a critical component of the propulsion systems in underwater vehicles. Particularly under conditions of low speed and high load, friction-induced vibration and wear often occur. Surface texturing technology has been proven to improve lubrication performance and reduce friction and wear; however, research on how different texture parameters affect friction-induced vibration and wear mechanisms remains insufficient. In this study, various texture patterns with different area ratios and aspect ratios were designed on the surface of water-lubricated rubber bearings. By combining these designs with an in situ observation system based on computer vision technology, the effects of texture parameters on bearing friction, vibration, and wear were thoroughly investigated. The experimental results show that surface textures play a critical role in improving hydrodynamic effects and stabilizing the lubrication film at the friction interface. Specifically, textures with a high area ratio (15%) and aspect ratio (3:1) exhibited the best vibration suppression effect, primarily due to the reduction in actual contact area. However, excessively high area ratios may lead to increased surface wear. This study concludes that a reasonable selection of texture area and aspect ratios can significantly reduce frictional force fluctuations and vibration amplitude, minimize surface wear, and extend bearing life.

The Direct Assimilation of Radar Reflectivity Data with a Two-Moment Microphysics Scheme for a Landfalling Typhoon in an OSSE Framework

Despite the well-known importance of radar data assimilation, there are limited studies on landfalling typhoons in terms of directly assimilating radar reflectivity data, especially using a reflectivity operator based on double-moment microphysics. In this study, radar reflectivity data assimilation experiments are conducted with an ensemble Kalman filter (EnKF), using simulated observations in an Observing System Simulation Experiment (OSSE) framework for the landfalling typhoon In-Fa. With an OSSE, it is convenient to analyze the impact of assimilation of radar reflectivity on analysis and forecast for various state variables, especially for hydrometeors. Our results show that the direct assimilation of radar reflectivity with EnKF does not introduce non-physical hydrometeors and is able to adjust well, not only to hydrometers, but also to some large-scale variables which are not directly related to reflectivity, especially in terms of temperature and vertical velocity. Though the most notable reduction in the Root Mean Square Errors (RMSEs) is observed through mixing the ratio of rainwater and snow, the analysis of other variables is also significantly improved with the accumulation of assimilation cycles. The correlation analysis reveals the strongest correlation between radar reflectivity data and hydrometeor-related variables as well as the correlation with certain large-scale variables, indicating that these cross-variables are updated well through the reliable multivariate ensemble covariance in the EnKF. As a result, an obvious improvement in typhoon intensity and precipitation forecast is obtained in the data assimilation experiment. The impact of assimilation on radar reflectivity can last for up to 15–16 h.

Investigating Tropical Cyclone Warm Core and Boundary Layer Structures with Constellation Observing System for Meteorology, Ionosphere, and Climate 2 Radio Occultation Data

The Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) collects data covering latitudes primarily between 40 degrees north and south, providing abundant data for tropical cyclone (TC) research. The radio occultation data provide valuable information on the boundary layer. However, quality control of the data within the boundary layer remains a challenging issue. The aim of this study is to obtain a more accurate COSMIC-2 radio occultation (RO) dataset through quality control (QC) and use this dataset to validate warm core structures and explore the planetary boundary layer (PBL) structures of TCs. In this study, COSMIC-2 data are used to analyze the distribution of the relative local spectral width (LSW) and the confidence parameter characterizing the random error of the bending angle. An LSW less than 20% is set as a data QC threshold, and the warm core and PBL composite structures of TCs at three intensities in the Northwest Pacific Ocean are investigated. We reproduce the warm core intensity and warm core height characteristics of TCs. In the radial direction of the typhoon eyewall, the impact height of the PBL increases from 3.45 km to 4 km, with the tropopause ranging from 160 hPa to 100 hPa. At the bottom of the troposphere, the variations in the positive and negative bias between the RO-detected and background field bending angles correspond well to the PBL heights, and the variations in the positive bias between the RO-detected and background field refractivity reach 14%. This research provides an effective QC method and reveals that the bending angle is sensitive to the PBL height.

Enhancing behavioral assessment: utilizing the BASC-3 student observation system in educational settings in Qatar

Direct observation of classroom behavior in young children enriches our understanding of their behavior in its natural context, informing both assessment and intervention strategies to foster supportive learning environments. However, a notable gap in the literature concerning effective direct observation tools specifically designed for this purpose remains challenging. This observational study utilized the digital format of the The Student Observation System (BASC-3-SOS; Reynolds & Kamphaus, 2015), enabling behavior recording via smartphones, tablets, or laptops. We conducted observations for 43 young children aged four and five in classroom settings. Thirty of these children had been previously identified with behavioral problems via the Strengths and Difficulties Questionnaire (SDQ, Child Psychol Psychiatry Allied Discip. 38(5), 581–6, 1997), while 13 were considered typically developing. Each child was observed over three 20-min sessions, totaling 60 min per child. The findings revealed a significant correlation between the SDQ scores and BASC-3-SOS assessments in adaptive and maladaptive behaviors. The results indicate that young children’s engagement and problem behaviors vary with teacher interactions and classroom activities. children identified with behavioral issues exhibited higher levels of maladaptive behaviors compared to their typically developing peers. The study presents the BASC-3-SOS as a promising tool for identifying general behavioral and emotional risks among young children in Qatar, suggesting its utility in enhancing early educational interventions.

Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System

Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R2) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions.

Snowy Dove: An open-sourcetoolkit for pre- processing of Chinese Gaofen series data.

Since the launch of the Chinese High-resolution Earth Observation System (CHEOS) program, China has strengthened its research and development in the field of satellite remote sensing. A large number of sensors has been or will be launched, providing very large data streams which all require processing of the engineering data, as provided by the instruments, to physical data which will be used for further processing and interpretation. To handle such large data streams we developed a one-click batch pre-processing toolkit for CHEOS remote sensing data as described in this paper. In this toolkit, IDL language and environment are used as the primary program combined with other programming languages developed in this research. In this paper, we first describe the Gaofen (GF) series data used in this research and then introduce the function design and realization of this one-click batch pre-processing toolkit. Some examples will be presented to illustrate the application of the toolkit to data from several CHEOS satellites.

Geodynamic hazard assessment for the archaeological park-museum by the town of Nikopol

This study was conducted to establish the engineering geological and hydrogeological conditions of the site and to assess the geodynamic hazard in relation to the design and construction of the archaeological park-museum and feeder road in the town of Nikopol (Bulgaria). The studied terrain occupies a river terrace and the foot of a delluvial slope with an elevation of 23-68 m. The tasks of the study are to identify: the geological structure of the slope, the area and depth range of the landslides, the location of the slip surface, the physical and mechanical properties of the engineering geological strata, the hydrogeological conditions, the current stability of landslides, the change of slope stability under the action of various factors. The ground investigation includes analysis of archival and published information on the area; geodetic survey of the landslide; engineering geological mapping; exploratory boreholes; geophysical surveys using the VES method; testing of soil and water samples; compilation of longitudinal engineering geological profiles and slope stability analysis using the computational models. To the south of the study site the slope is affected by an ancient landslide with a volume of about 30 million m3 developed on the right valley slope of the Danube River in the section between the plateau and the river terrace. Consistent landslides occur in its lower part. Slope stability calculations show that the landslides are triggered by a rise in the groundwater level, an earthquake and undermining of the slope base. Rock-falls has developed on the steep rock slopes, composed of moderately to highly weathered and fractured cherty and glauconitic limestones. The size of the rockfall blocks is in the range 0.3-0.8 m3 and their transport distance reaches from 10 m to 50 m from the base of the slope. Stabilization measures and facilities are proposed to strengthen the landslides and rockfalls in the considered section of the slope. For the landslides it is proposed to implement a counterfort embankment on which to build the feeder road, drainage ribs and drainage ditches. For protection of the buildings from collapsing rock debris, an anchored high-strength net is recommended. The design takes into account the trajectory of the falling blocks and their kinetic energy. For monitoring the condition of the landslides and rockfalls, as well as for evaluating the effectiveness of the implemented strengthening, drainage and coastal protection structures, a control and measurement system for regime observation was built.

RBCKF-Based Vehicle State Estimation by Adaptive Weighted Fusion Strategy Considering Composite-State Tire Model

The acquisition of vehicle driving status information is a key function of vehicle dynamics systems, and research on high-precision and high-reliability estimation of key vehicle states has significant value. To improve the state observation effect, a vehicle sideslip angle estimation method adopting a robust bias compensation Kalman filter and adaptive weight fusion strategy is proposed. On the basis of the extended Kalman filter algorithm, and with the goals of estimation exactitude and robustness, considering the potential signal deviation, a vehicle state robust deviation compensation Kalman filter estimation algorithm considering bias compensation and residual covariance matrix weighting is proposed. Meanwhile, considering the adaptive and dynamic adjustment capabilities of the observation system in complex state-change scenarios, an estimation strategy based on adaptive weight fusion and a model-based estimator is proposed. The results confirm that the robust bias compensation Kalman filter can ensure estimation exactitude and robustness when the vehicle state fluctuates greatly, and the proposed fusion strategy can ensure that the vehicle maintains optimal estimation performance during operating condition switching.

The Future of Radar Space Observation in Europe—Major Upgrade of the Tracking and Imaging Radar (TIRA)

The use of near-Earth space has grown dramatically during the last decades, resulting in thousands of active and inactive satellites and a huge amount of space debris. To observe and monitor the near-Earth space environment, radar systems play a major role as they can be operated at any time and under any weather conditions. The Tracking and Imaging Radar (TIRA) is one of the largest space observation radars in the world. It consists of a 34m Cassegrain antenna, a precise tracking radar, and a high-resolution imaging radar. Since the 1990s, TIRA contributes to the field of space domain awareness by tracking and imaging space objects and by monitoring the debris population. Due to new technologies, modern satellites become smaller, and satellite extensions become more compact. Thus, sensitive high-resolution space observation systems are needed to detect, track, and image these space objects. To fulfill these requirements, TIRA is undergoing a major upgrade. The current imaging radar in the Ku band will be replaced by a new radar with improved geometrical and radiometric resolution operating in the Ka band. Due to its wideband fully polarimetric capability, the new imaging radar will increase the analysis and characterization of space objects. In addition, the tracking radar in the L band is also being currently refurbished. Through its novel modular structure and open design, highly flexible radar modes and precise tracking concepts can be efficiently implemented for enhanced space domain awareness. The new TIRA system will mark the start of a new era for space observation with radar in Europe.

Flow-dependent observation errors for greenhouse gas inversions in an ensemble Kalman smoother

Abstract. Atmospheric inverse modeling is the process of estimating emissions from atmospheric observations by minimizing a cost function, which includes a term describing the difference between simulated and observed concentrations. The minimization of this difference is typically limited by uncertainties in the atmospheric transport model rather than by uncertainties in the observations. In this study, we showcase how a temporally varying, flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation through idealized experiments using an ensemble Kalman smoother system. We use the estimation of European CH4 emissions from the in situ measurement network as an example, but we also demonstrate the additional benefits for trace gases with more localized sources, such as SF6. The uncertainty in flow-dependent transport is determined using meteorological ensemble simulations that are perturbed by physics and driven at the boundaries by an analysis ensemble from a global meteorology and a CH4 simulation. The impact of direct representation of temporally varying transport uncertainties in atmospheric inversions is then investigated in an observation system simulation experiment framework in various setups and for different flux signals. We show that the uncertainty in the transport model varies significantly in space and time and that it is generally highest during nighttime. We apply inversions using only afternoon observations, as is common practice, but also explore the option of assimilating hourly data irrespective of the hour of day using a filter based on transport uncertainty and taking into account the temporal covariances. Our findings indicate that incorporating flow-dependent uncertainties in inversion techniques leads to more accurate estimates of GHG emissions. Differences between estimated and true emissions could be reduced more effectively by 9 % to 82 %, with generally larger improvements for the SF6 inversion problem and for the more challenging setup with small flux signals.

Smart Agriculture Crop Management Warehouse

In a developing country like India, agriculture is one the most important sectors in terms of financial gain and providing healthy foods to survive. Agriculture is the primary occupation in our country for ages and most of the population depend on it. Now-a-days farmers are facing huge losses due to some storage requirements, while storing agricultural products. A Farmer should have knowledge about the condition of their agricultural crops. Farmers are keeping their agricultural crops in warehouses to increase the lifetime of food grains. Food plays a significant vital role in life, once it involves food security that is littered with each food loss and food wastages. Reports suggests that almost fifty proportion of the food made world-wide doesn't even reach the individuals as they're wasted throughout harvest, transportation, or storage. One in all the point that threats to the farmers is that the loss throughout storage of crops in granaries and warehouses. If crop losses are reduced, it'll mechanically increase the number of food accessibility. To solve this problem one solution is that this project includes smart warehouse management and monitoring farming field, which includes temperature maintenance, humidity maintenance, fire alarms, stock measurement, Pest control. To assist and help the farmers who protects the crops by storing in warehouse, a fully automated enabled observation system is planned to be deployed in remote areas wherever the accessibility is incredibly minimum for farmers with smart storage facilities to scale back food losses and increase food safety. Keywords: Agricultural crops monitoring and controlling, fully automated technology, Sensors, Warehouse management system.

The Aerodynamic Assessment and Shot Point Position Accuracy of UAV Seismic Source

An unmanned aerial vehicle (UAV) seismic source, suitable for complex terrains due to its eco-friendly, cost-effective, and efficient nature, operates by hovering and releasing impact objects to generate seismic waves. The aerodynamic behavior of these impact objects is vital for shotpoint position accuracy and drag magnitude. Excessive drag will result in the loss of seismic source energy, whereas the deviations of the shotpoint position will disrupt the geometric structure of the observation system, significantly impacting the quality of seismic data acquisition. Through theoretical calculations, the influencing factors of air resistance and cross-wind deflection are analyzed. Air resistance increases with the increase in mass, cross-sectional area, speed, and drag coefficient. Increasing the mass and throwing height can increase the upper limit of the output, but after the air resistance and gravity are balanced, the output kinetic energy of the drone’s seismic source will no longer increase when the throwing height is increased. Using computational fluid dynamics simulations and field tests, this study examines the aerodynamics of four prevalent impact object designs. The results show that shuttle and spherical structures have the least drag during descent and are less influenced by crosswinds, thus being optimal for this application. Field measurement data are also presented for reference.

KISS: Instrument Description and Performance

Kinetic inductance detectors (KIDs) have been proven as reliable systems for astrophysical observations, especially in the millimeter range. Their compact size enables them to optimally fill the focal plane, thus boosting sensitivity. The KIDs Interferometric Spectral Surveyor (KISS) instrument is a millimeter camera that consists of two KID arrays of 316 pixels each coupled to a Martin–Puplett interferometer (MPI). The addition of the MPI grants the KID camera the ability to provide spectral information in the 100 and 300 GHz range. In this paper, we report the main properties of the KISS instrument and its observations. We also describe the calibration and data analysis procedures used. We present a complete model of the observed data including the sky signal and several identified systematics. We have developed a full photometric and spectroscopic data analysis pipeline that translates our observations into science-ready products. We show examples of the results of this pipeline on selected sources: the Moon, Jupiter, and Venus. We note the presence of a deficit of response with respect to expectations and laboratory measurements. The detector's noise level is consistent with values obtained during laboratory measurements, pointing to a suboptimal coupling between the instrument and the telescope as the most probable origin for the problem. This deficit is large enough to prevent the detection of galaxy clusters, which were KISS's main scientific objective. Nevertheless, we have demonstrated the feasibility of this kind of instrument in the prospect of other KID interferometers (such as the CONCERTO instrument). In this regard, we have developed key instrumental technologies such as optical conception, readout electronics, and raw calibration procedures, as well as adapted data analysis procedures.

Experimental study of hydrodynamic characteristics in three-phase coarse particle fluidized flotation column

Understanding multiphase flow regimes and hydrodynamic characteristics in the three-phase coarse particle fluidized flotation column (TFC) is crucial in mineral processing. This study systematically investigated the influence of gas velocity (Ug) and water velocity (Uw) on the flow regimes in TFC, focusing on the identification and hydrodynamic characteristics of different flow regimes in the freeboard and bulk fluidized bed regions. Three distinct flow regimes (heterogeneous flow, pseudo-homogeneous flow and mono-homogeneous flow) and two transition water velocities (transitions from a heterogeneous flow regime to a pseudo-homogeneous flow (Ut1) and transitions from a pseudo-homogeneous flow to a mono-disperse homogeneous flow regime (Ut2)) were identified in the freeboard region based on the visual observation and bubble characteristic parameter acquisition system (BVW-2) monitoring the variations in the bubble size distribution. Visual observation combined with electrical resistance tomography (ERT) monitoring the solid concentration variations facilitated accurate determination of three flow regimes (fixed bed regime, agitated bed regime, and complete fluidization regime) and two transition points (minimum agitation water velocity (ULma) and minimum fluidization water velocity (ULmf)) in the bulk fluidized bed region. Furthermore, statistical and spectral methods were used to analyze pressure fluctuation signals and characterize fluidization behavior across different flow regimes, including the average pressure fluctuation (ΔP¯), the probability density function (PDF), normalized standard deviation (δ), kurtosis (K), skewness (S), autocorrelation function (ACF), power spectral density (PSD) and average cycle frequency (fc). The results indicated that it is the changes in dominant flow structures within the three-phase fluidized bed, such as variations in bubble behavior and particle movement under different Ug and Uw, that lead to differences in flow characteristics, thereby forming distinct flow regimes.

Multiresolution Analysis of HRRR Meteorological Parameters and GOES-R AOD for Hourly PM2.5 Prediction.

High-resolution, comprehensive exposure data are crucial for accurately estimating the human health impact of PM2.5. In recent years, satellite remote sensing data have been increasingly utilized in PM2.5 models to overcome the limited spatial coverage of ground monitoring stations. However, data gaps in satellite-retrieved parameters such as aerosol optical depth (AOD), the sparsity of regulatory air quality monitors for model training, and nonlinear relationships between PM2.5 and meteorological conditions can affect model performance and cause data gaps in most existing PM2.5 models. In this study, spatial gaps in AOD obtained from Geostationary Operational Environmental Satellite-16 are filled using Goddard Earth Observing System Composition Forecasting AOD estimations. Furthermore, to improve model performance, meteorological predictors such as temperature from the High-Resolution Rapid Refresh model are preprocessed using Daubechies wavelet to extract low and high frequency components. The spatially gap-filled AOD, along with meteorological data, are ingested into a machine learning model to predict hourly PM2.5 at a 1 km spatial resolution in California. The model evaluation metrics (OOB (out-of-bag) R2 = 0.86 and RMSE (root-mean-square error) = 9.27 μg/m3 and 10-fold spatial cross-validation R2 = 0.82 and RMSE = 9.82 μg/m3) demonstrate the model's reliability in predicting ambient PM2.5, especially for states like California that experience frequent episodes of wildfires.

Ensemble Kalman Filter Data Assimilation into the Surface Flux Transport Model to Infer Surface Flows: An Observing System Simulation Experiment

Knowledge of the global magnetic field distribution and its evolution on the Sun’s surface is crucial for modeling the coronal magnetic field, understanding the solar wind dynamics, computing the heliospheric open flux distribution, and predicting the solar cycle strength. As the far side of the Sun cannot be observed directly and high-latitude observations always suffer from projection effects, we often rely on surface flux transport (SFT) simulations to model the long-term global magnetic field distribution. Meridional circulation, the large-scale north–south component of the surface flow profile, is one of the key components of the SFT simulation that requires further constraints near high latitudes. Prediction of the photospheric magnetic field distribution requires knowledge of the flow profile in the future, which demands reconstruction of that same flow at the current time so that it can be estimated at a later time. By performing Observing System Simulation Experiments, we demonstrate how the ensemble Kalman filter technique, when used with an SFT model, can be utilized to make “posterior” estimates of flow profiles into the future that can be used to drive the model forward to forecast the photospheric magnetic field distribution.

Impact of Assimilating Uncrewed Aircraft System Observations on River Valley Fog Prediction

Abstract The impact of assimilating targeted uncrewed aircraft system (UAS) observations on the prediction of radiation and river valley fog is assessed using observing system experiments (OSEs). Two multirotor UASs were deployed during Frequent in situ Observations above Ground for Modeling and Advanced Prediction of fog (FOGMAP) which took place during the summer of 2022 in northern Kentucky. Targeted UAS missions were flown to sample the spatiotemporal variability of temperature and moisture in the vicinity of the Cincinnati/Northern Kentucky International Airport. During each mission, the UAS performed near-continuous profiling at two locations between the surface to 120 m AGL throughout the night. Data denial experiments were performed using the ensemble adjustment Kalman filter available in NSF NCAR’s Data Assimilation Research Testbed (DART) to determine the impact of assimilating UAS observations on the skill of analyses and forecasts issued during potential fog events. Simulations that only assimilated conventional observations tended to have a dry bias in the analyses and forecasts. The dry bias in the analyses was reduced in experiments that assimilated UAS observations leading to improved probabilistic predictions of fog. Sensitivity tests revealed that the ensemble mean analyses were improved when assimilating UAS observations of specific humidity rather than relative humidity (RH) due to the existence of a cold bias near the surface and the negative covariance between RH and temperature. It was also found that either the assumed observation error variance of (1 g kg−1)2 or the ensemble spread of the background specific humidity was too large since their sum tended to overestimate the root-mean-square error (RMSE) of the predicted ensemble mean values.

Prediction of atmospheric temperature in Sacramento area based on ARIMA and ETS models

Abstract. As global temperatures increased by 1.1 Celsius degrees, there has unprecedented shifts in climate systems. With the rising impact of global warming, exploring the warming trend helps to better understand and maintain the local environment and economy. This study focuses on predicting atmospheric temperature in the Sacramento area using ARIMA and ETS models. The research uses the temperature data from Sacramento Airport's Automated Surface Observing System (ASOS) and explores the prediction performance of ARIMA, ETS, and ARIMAX models in predicting daily average temperatures. The results indicate the ARIMAX (1,1,1) model is the most suitable for forecasting this temperature data with the lowest AIC and RMSE values. However, there are still challenges, in particular the dependence of the ARIMAX model on future exogenous variables, which leads to the forecast outcomes less accurate. To enable this ARIMAX models have better prediction performance, incorporating more exogenous variables is a potential solution. Therefore, the methods discussed in this paper provides ideas for the atmospheric temperature forecasting and points out the direction for further research.

Multi-Sensor Photoelectric Fire Alarm Device Implementation for Early Fire Detection in Campsites

With the growing popularity of leisure activities such as camping and glamping, the incidence of fires at camping sites has increased. This study focuses on improving the effectiveness of photoelectric fire alarm devices by incorporating temperature and humidity data for early fire detection in confined spaces, such as campsites. This study proposes a novel multi-sensor fire alarm system that dynamically adjusts fire detection threshold values based on temperature and humidity data collected by unmanned automatic weather observation systems. The prototype, which was implemented using Raspberry Pi and multiple sensors, demonstrated approximately 20% faster fire detection speed than existing photoelectric fire alarm systems, as verified through experiments in a simulated camping environment. The proposed approach is expected to advance fire alarm systems, enabling faster and more accurate fire detection in diverse environments, particularly at campsites.

Towards modelling instructional quality for music class-rooms: Exploring subject-specific application of the TA-LIS/GTI generic framework

Arts subjects have so far been absent from large-scale studies on instructional quality as well as from related methodological development. The purpose of this article is to explore whether an observation scheme that builds on generic theoretical models and aims to capture instructional quality across school subjects might be meaningfully employed in general music education so that it could serve as a basis for empirical research and reflective practice. We evaluated instructional quality criteria proposed in the video observation system from the study Global Teaching InSights, the third cycle of OECD’s large-scale Teaching and Learning International Survey (TALIS/GTI) by exploring the system’s observation codes for analysis of music lessons on video and comparing the criteria with quality criteria from general music education. We found that the generic models are helpful and may have more to offer both research and practice in music education than we initially expected. The risk when attempting to apply quality criteria that are intended to be generic is that those aspects may be overemphasised while criteria that are essential for music education are pushed out of sight. However, we argue that modelling instructional quality in music classrooms is worthwhile in itself and that the friction points where generic criteria do not fit our subject are particularly interesting. A systematic discussion of quality criteria can sharpen our views and understandings of general music education and connect music education research with interdisciplinary discourse and with a much-needed, broad dialogue within the educational sciences.