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Black Carbon (BC) is a potent air pollutant with severe implications for both health and climate. Emitted through partial combustion of petroleum based fuel and biomass materials, BC particles are characterized by their fine particulate nature and ability to absorb sunlight, posing serious problems to human health and climate. To effectively combat these growing concerns, accurate evaluation and measurement of BC emissions are becoming critical areas of research. This review examines BC emissions from various sources and explores advances in its measurement techniques and regional mitigation strategies. It also advocates for integrating BC emissions mitigation into broader climate and health policies. BC can be measured in a number of ways to test the effectiveness of a mitigation strategy. Among these methods, sensor-based approaches have revolutionized the way BC is measured, tracked, and targeted for remediation.

India, being a rapidly industrializing nation with dense urban centers, grapples with significant BC emissions. Several notable programs and initiatives have been established to tackle the excessive release of BC in the country. The National Clean Air Programme (NCAP), launched by the Government of India, strives to reduce atmospheric pollution through city-specific action plans and technological interventions. The Pradhan Mantri Ujjwala Yojana intends to finance clean sources of cooking fuel for vulnerable populations, curbing BC emissions from traditional biomass burning. Continuous research and collaboration on BC emissions hold the potential to significantly reduce BC emissions' effects on human health and the global environment.

Online weather forecast system usually works over a large geographical area. However, it has been noticed that, due to uneven altitude zones specially in the hilly areas, there are some flaws in accuracy found in meteorological parameters of smaller regions within same coordinates. This paper presents a solar powered local weather measurement and prediction system both for plains and hilly regions using Internet of Things (IoT) and Random Forest Regressor. An ESP32 microcontroller, paired with the temperature, humidity and Atmospheric pressure sensors used to facilitate real-time meteorological data monitoring. The proposed system incorporates online weather data for comparison with local-sensor parameters within same coordinates to track out the minor changes. These minor changes feed into the Random-Forest Regressor algorithm for improvement in measurement and prediction accuracy. With 98% calibrated sensor accuracy, this scalable prototype minimizes the errors of local weather parameters of small region which is beneficial to local agriculture practices like terrace cultivation, contour ploughing; meteorological surveys and future researches.

A coaxial twin resistor thermistor mount has been realized as a national standard of RF Voltage at CSIR-National Physical Laboratory, India (NPLI). In electrical metrology, particularly in radio frequency (RF) metrology, radio-frequency (RF) voltage is an important parameter. Metrological accuracy in RF power measurements is critical for various scientific and industrial applications, including telecommunications, medical devices, and radar systems. This paper presents measurement data for the validation of the RF Voltage primary standard at CSIR-National Physical Laboratory, India (NPLI) i.e. twin resistance coaxial power mount power sensor with 50 Ω type N connector which has been carried out between Physikalisch-Technische Bundesanstalt (PTB Germany) and NPLI in the frequency range of 1–1000 MHz. The measurements were carried out at PTB Germany in 2022 and at NPLI in 2021 and 2024. The RF Voltage probe which is having a 50 Ω type N connector, is based on the power substitution technique, where the heating effect produced by RF power is compared with the equivalent DC power. The RF Voltage primary standard has been assigned the RF–DC transfer difference from 1 to 1000 MHz. The RF–DC transfer difference values assigned by NPLI and PTB show good agreement across the investigated frequency range. The RF–DC transfer differences evaluated between the two laboratories remain within the associated expanded measurement uncertainties.

In fast-growing developing zones like Haryana, India, there is an increasing problem of air pollution that is a threat to the population’s health and environmental sustainability. The paper researches the spatial and temporal distributions of major air pollutants, including PM₁₀, PM_2.5, NO₂, and SO₂, from 2019 to 2023 with the help of the data provided at 24 Continuous Ambient Air Quality monitors (CAAQMS). The spatial analysis based on Geographic Information System (GIS) was done on the pollutant distribution and hotspots of the regions using the ArcGIS 10.4 Interpolation Interface, Inverse Distance Weighting (IDW) method. Pearson correlation analysis and descriptive statistics were used to analyse pollutant variability and pollutant relationships. The findings indicate that there is a significant decrease in the concentration of pollutants throughout the COVID-19 lockdown, as NO₂, PM_2.5, PM₁₀, and SO₂ pollutants decreased by 60.9%, 15.31%, 10.26% and 31.6%, respectively. Regardless of such an overall increase in the concentration, the urban-industrial areas of Faridabad, Gurugram, and Panipat always registered higher levels of pollutant concentrations, which showed the presence of localised sources of emissions. The results point to the efficiency of short-term emission cuts during lockdown and emphasise the usefulness of region-specific approaches to pollution management in enhancing the therapeutic air quality of rapidly urbanising areas.

The transcutaneous pacer analyzer is used to verify the performance of transcutaneous pacers, which provide a temporary method of cardiac pacing during various clinical cardiac emergencies. Establishing a calibration procedure for the transcutaneous pacer analyzer is essential to ensure the accuracy and quality of performance verification of transcutaneous pacers. This paper presents the developed calibration methodology for a transcutaneous pacer analyzer, along with a systematic approach for the estimation of measurement uncertainty for important parameters such as pulse energy, pulse current, pulse width and pulse rate using a calibration setup traceable to national standards. The measurement uncertainty associated with these parameters is evaluated by considering various contributing factors. The expanded uncertainty evaluated for resistance at 50 Ω, pulse energy of 2.5–22.5 mJ, pulse current of 50–150 mA, pulse width of 20 ms and pulse rate of 1–3.5 Hz are ± 0.5 mΩ, ± (0.03–0.3) mJ, ± (0.2–0.4) mA, ± 0.02 ms and ± 0.01 Hz respectively. Such a comprehensive analysis on measurement uncertainty evaluation has not been reported so far.

The traditional manufacturing system involves a separate inspection of the roundness error of the machined component which increases the overall manufacturing time and cost of the component. The accuracy of the roundness error in the traditional roundness measuring system is affected by the component’s true centering. A system for on-machine roundness measurement using machine vision was developed to overcome such issues. During the measurement process, a novel approach was used in which, on the same image, calibration and measurement were performed. In this study, a novel Cyclic-ANN methodology was proposed to predict the diameter and roundness errors of the component. The predicted value of the roundness error using Cyclic-ANN methodology was found to be 0.0142 mm which was compared with the CMM measurement of the component with a value of 0.0154 mm. Thus, experimental results show the robustness, accuracy, and simplicity of the developed system. The proposed methodology estimates the ‘actual working roundness error’, allowing the design engineer to modify the component’s design tolerance and the manufacturing engineer to modify the machining operations to reduce manufacturing costs.

This work investigates the dimensional accuracy, shrinkage behavior, and dimensional measurement uncertainty of sintered H13 tool steel specimen manufactured through Metal Fused Filament Fabrication (M-FFF), implemented in this study through a Markforged Metal X (Atomic diffusion additive manufacturing based) system. Linear, circular, and angular dimensions were characterized through a high-accuracy Coordinate Measuring Machine (CMM), and uncertainty budgets were prepared in accordance with the Law of Propagation of Uncertainty (LPU) method. Results indicate anisotropic shrinkage with the X–Y plane undergoing more shrinkage (17.0–20.0%) than the Z-direction (15.0–15.5%), due to filament packing density, layer stacking limitations, and thermal gradients during debinding and sintering. Typical uncertainty evaluation for linear dimensions of the sintered samples demonstrated the maximum combined standard uncertainty of ± 14.008 µm, whereas the expanded uncertainty computed was ± 38.8 µm (at a coverage factor k = 2.77, corresponding to the confidence level of 95% for a Gaussian distribution). Angular dimensions showed the highest deviations up to 0.23° and, maximum combined uncertainty of ± 363 Arc seconds and the expanded uncertainty of ± 1006 Arc seconds (at a coverage factor k = 2.77, corresponding to the confidence level of 95% for a Gaussian distribution). The investigation points to the significant effect of anisotropic shrinkage on dimensional deviation and underpins the need for sound uncertainty quantification to separate the true process-induced deformation from measurement randomness. For high-precision applications such as molds and cutting tools, post-processing or enhanced process control is recommended to ensure compliance with dimensional and angular tolerance requirements.

Long-term monitoring (2015–2019) of atmospheric trace gases (NH₃, NO, NO₂ and SO₂) and aerosols (PM_2.5 and PM₁₀) was carried out at Palampur, Himachal Pradesh (HP) over the western Himalayan region of India to explore the influence of trace gases and meteorology on secondary inorganic aerosol (SIA) formation. During the entire monitoring period, the mean concentrations of NH₃, NO, NO₂ and SO₂ were 13.4 ± 8.9 µg m⁻³, 15.2 ± 12.6 µg m⁻³, 13.0 ± 9.8 µg m⁻³ and 7.1 ± 5.1 µg m⁻³, respectively, whereas the mean concentrations of PM_2.5 and PM₁₀ were 19.3 ± 14.5 µg m⁻³ and 39.4 ± 22.9 µg m⁻³, respectively. Although the annual mean concentrations of these trace gases and aerosols were well within the National Ambient Air Quality Standards (NAAQS) during 2015–2019, however, the daily mean value of these pollutants (NO, NO₂, SO₂, PM_2.5 and PM₁₀) occasionally breached the NAAQS (prescribed for the ecologically sensitive area as well as rural areas). The concentrations of trace gases and aerosols showed significant diurnal, seasonal and monthly variations during the monitoring period. The mean concentrations of all the trace gases were observed highest in winter (except SO₂) whereas the lowest in the monsoon season. The mass concentrations of PM_2.5 (23.1 ± 11.2 µg m⁻³) and PM₁₀ (47.5 ± 13.3 µg m⁻³) were recorded highest in summer and lowest in monsoon seasons. The mean PM_2.5/PM₁₀ ratio during winter, summer and post-monsoon seasons were estimated to be 0.52, 0.49, and 0.52, respectively; indicating almost 50% contribution of fine fraction of aerosol in PM₁₀ (except monsoon season) supporting the influence of secondary formation. The SIA formation through gas-to-particle process over the study site may leads the dense haze formation during winter and colder months. Result reveals the inter-annual variability of trace gases (NH₃, NO, NO₂ and SO₂) and aerosols (PM_2.5 and PM₁₀) concentrations over the monitoring site in the western Himalayas. The surface wind analysis with wind direction revealed the local activities/tourism, agricultural activities, vehicular emissions and combustion to be the main sources of trace gases and aerosols over the western Himalayas. The linear positive relationship of NH₃ with NO, SO₂, and PM_2.5 suggests the influence of trace gases in the SIA formation at the study site. The present study on the interaction of ambient trace gases and aerosols over the Himalayas region is a valuable document for the policymakers for further mitigation and reduction in pollutants loading to improve the air quality of the Himalayan region.

The recent outbreaks solar energy generation, some challenges have popped up owing to solar intermittent behaviour, require an accurate estimation of the worldwide solar radiation. In this reference AI-ML algorithms are gaining popularity and effectiveness in calculating solar radiation. Since collinearity might cause troubles such as unstable parameter estimation, inaccurate models, and poor predictive performance. However, multiple linearity tends to become a major issue in linear models. To address these issues, the spearman rank Correlation (SRC) and variance inflation factor (VIF) was introduced, and a new variable selection method called Spearman Rank Correlation-Variance Inflation Factor (SRC-VIF) was proposed, in addition to this article presents a better stack ensemble with GRU (SE-GRU) regressor include the machine learning models like, Random Forest Regressor (RF), Support Vector Regressor (SVR), Multilayer perceptron Regressor (MLP), K-Nearest Neighbor Regressor (KNN), Light Gradient Boost Machine (LGMs), CatBoost Regressor (CB), and Extreme Gradient Boost (XGBost), while the ensemble stacking with GRU regressor is used as meta learners. The ensemble technique includes stacking, blending, bagging and boosting methodology. In this paper use above all technique for the prediction of solar radiation of Agra (India) location. With the help of this technique we precise the result and able to minimise the errors in predicted value such as Mean Absolute Error (MAE) 15–56%, Root Mean Squared error (RMSE) 22–60% and R squared error (R2) 26–45%. Ultimately, we can say that the ensemble technique helps to decrease prediction errors and improves planning for intermittent solar resources.

This paper presents a downhole instrument that leverages a dual-position north-seeking technique to achieve high-precision six-degree-of-freedom (6-DoF) navigation in borehole environments where external positioning signals are unavailable. The instrument integrates a MEMS-based inertial measurement unit (IMU) with a single-axis optical gyroscope, together with multiple environmental sensors, forming a compact multi-parameter measurement tool. Through a two-orientation azimuth determination method, the system mitigates gyroscope bias and resolves heading ambiguity. We reformulate the theoretical basis of this dual-position gyrocompassing approach and embed it within the instrument’s sensor-fusion architecture to continuously estimate borehole attitude—inclination, azimuth, and toolface—together with incremental displacement in real time. Analytical modeling, simulation, and laboratory experiments confirm robust and accurate north-seeking performance: after calibration, azimuth errors are typically.