MAPAN最新文献

{"title":"Evaluating Time Synchronization Performance on an Edge Device Using Geographically Distributed NTP Servers","authors":"Divya Singh Yadav,&nbsp;Amit Kaushik,&nbsp;Ashish Agarwal,&nbsp;Meher Wan,&nbsp;Subhasis Panja","doi":"10.1007/s12647-026-00912-7","DOIUrl":"10.1007/s12647-026-00912-7","url":null,"abstract":"<div><p>Time synchronization is essential for Internet of Things (IoT) and edge computing applications that require accurate time stamping and coordinated operation. Low-cost edge devices employ quartz-based oscillators, which exhibit clock drift over time and Network Time Protocol (NTP) is widely used to mitigate this kind of drift and synchronized to the reference clock. However, achievable NTP synchronization performance is strongly influenced by network conditions and the geographic location of the reference time server. This article presents a study over long period of NTP-based time synchronization on a low-cost edge device, i.e., Raspberry Pi using chrony and geographically distributed NTP servers. Clock offsets and round-trip network delays are recorded at one-minute intervals using chrony tracking statistics, resulting in independent datasets for each server. The collected data are analyzed for evaluating synchronization accuracy, stability, and client-side synchronization uncertainty under realistic network conditions. Client-side uncertainty evaluation has been done for the practical measure of synchronization reliability as experienced by an edge device. The uncertainty analysis also offers a realistic basis for relative performance assessment and informed NTP server selection in IoT and edge computing deployments.</p></div>","PeriodicalId":689,"journal":{"name":"MAPAN","volume":"41 2","pages":"415 - 424"},"PeriodicalIF":1.3,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preparation and Certification of Polystyrene Reference Material for Density Measurements Using an Inter-laboratory Comparison Approach","authors":"A. Abdel-Hakim,&nbsp;Randa N. Yamani","doi":"10.1007/s12647-025-00874-2","DOIUrl":"10.1007/s12647-025-00874-2","url":null,"abstract":"<div><p>This article outlines the procedures for preparing and certifying polystyrene certified reference materials (CRMs) for density measurements. The National Institute of Standards (NIS) has certified this CRM. The material is obtained from a commercially available polystyrene source. The certification process involved assessing the material’s homogeneity and stability, along with characterization through an interlaboratory comparison (ILC) method. The new CRM has been validated for its density value (measured in kg/m³). It is designed for use in quality assurance processes for measuring density in polymers and similar matrices. Expanded uncertainties are derived from calculated uncertainties as outlined in the Guide to the Expression of Uncertainty in Measurement (GUM). These uncertainties utilize a coverage factor of k = 2, which corresponds to a confidence level of approximately 95%.</p></div>","PeriodicalId":689,"journal":{"name":"MAPAN","volume":"41 2","pages":"561 - 574"},"PeriodicalIF":1.3,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148061056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of Valve and Gudgeon Pin of an Internal Combustion Engine","authors":"Sandeep Kumar,&nbsp;Mayank Mathur,&nbsp;Gopal Jee Verma,&nbsp;Kuldeep Kumar,&nbsp;Naveen Garg,&nbsp;G. S. Parvathi,&nbsp;Mukesh Jewariya","doi":"10.1007/s12647-026-00909-2","DOIUrl":"10.1007/s12647-026-00909-2","url":null,"abstract":"<div><p>This study presents a comprehensive characterization of critical internal combustion engine (ICE) components, namely the engine valve and gudgeon pin, which are subjected to severe mechanical loads, friction, and thermal stresses during operation. The surface integrity and material properties of these components play a decisive role in engine efficiency, wear resistance, and service life. Vickers microhardness testing was performed at multiple indentation loads to evaluate near-surface mechanical behaviour, while crystallographic characteristics were investigated using X-ray diffraction (XRD) analysis. Surface texture was quantitatively analyzed using a stylus-based profilometer to determine amplitude, spacing, and functional roughness parameters. The results indicate that both components exhibit high hardness suitable for hostile engine environments, with XRD revealing predominantly ferritic phases and low microstrain, indicative of good crystalline quality. Roughness analysis shows optimized surface finishes, with material bearing ratio parameters (Mr1 and Mr2) suggesting favourable load-bearing capacity and lubrication retention. The findings highlight the importance of controlled surface roughness and material selection in improving the functional performance and reliability of ICE components, and they provide a foundation for future tribological and engine-level performance studies.</p></div>","PeriodicalId":689,"journal":{"name":"MAPAN","volume":"41 2","pages":"549 - 559"},"PeriodicalIF":1.3,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing Accurate Methods for Characterizing Soil Reference Materials: Measuring Iron, Copper, Phosphorus, Sulfur, and Chlorides","authors":"Johanna P. Abella,&nbsp;Diego A. Ahumada,&nbsp;María J. Martínez","doi":"10.1007/s12647-026-00885-7","DOIUrl":"10.1007/s12647-026-00885-7","url":null,"abstract":"<div><p>The use of reference materials is essential to ensure the quality and reliability of measurement processes. In soil analysis, the characterization of operationally defined measurands is challenging due to the high variability of soil properties and the lack of standardized methods. The objective of this study is to develop and validate reliable measurement techniques specifically designed for the characterization of soil reference materials, focusing on the quantification of available iron, copper, phosphorus, sulfur, and chlorides. For this purpose, methods such as inductively coupled plasma mass spectrometry (ICP-MS) for metals, ion chromatography for anions, and UV–Vis spectrophotometry for phosphorus were employed. These methods were evaluated in terms of selectivity, linearity, precision under repeatability conditions, and bias. The results showed that the methods are suitable for the characterization of reference materials, achieving precisions below 3.5%, recoveries between 95 and 100%, and relative uncertainties under 4%. Additionally, the methods were applied to assess the homogeneity of two distinct reference materials, revealing homogeneity ranges between 0.48 and 5%. These methods contribute to the standardization of soil analysis, facilitating the production of reliable reference materials and supporting quality assurance in environmental and agricultural measurements.</p></div>","PeriodicalId":689,"journal":{"name":"MAPAN","volume":"41 2","pages":"539 - 548"},"PeriodicalIF":1.3,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Investigation and Statistical Analysis Improve the Metrological Reliability of Forehead Infrared Radiation Thermometers","authors":"Pedro Diniz,&nbsp;Rafael Farias,&nbsp;Klaus Quelhas,&nbsp;Rodrigo Costa-Felix","doi":"10.1007/s12647-026-00891-9","DOIUrl":"10.1007/s12647-026-00891-9","url":null,"abstract":"<div><p>Forehead Infrared Radiation Thermometers (FIRTs) are widely employed in healthcare settings and access control due to their practicality. However, concerns persist regarding their metrological reliability, particularly in clinical applications. These devices operate in two modes: object and adjusted (body temperature). However, current evaluation methodologies lack standardised procedures and statistical criteria for assessing clinical performance. This study aimed to develop a clinical investigation protocol and establish robust statistical criteria for evaluating the metrological performance of FIRTs in adjusted mode. Eighteen FIRTs from ten different models were assessed through clinical measurements conducted on 106 emergency room patients. Simultaneously, a digital contact thermometer was used to obtain reference body temperatures. The analysis included four statistical approaches: clinical tendency analysis (mean error and standard deviation), outcome agreement tests (paired t-test and Wilcoxon signed-rank test), Bland–Altman analysis, and effect size assessment. Only six devices (33%) met the performance criteria across all volunteers. Among febrile patients (≥ 37.5 °C), seven thermometers performed satisfactorily, while only one device met the requirements for non-febrile patients (&lt; 37.5 °C). The proposed clinical methodology proved effective for performance assessment, and the combination of statistical tools facilitated the establishment of evaluation criteria. Among these, the effect size analysis provided the most comprehensive insights. The significant disparity in results between febrile and non-febrile groups suggests a potential influence of correction algorithms embedded in the devices.</p></div>","PeriodicalId":689,"journal":{"name":"MAPAN","volume":"41 1","pages":"211 - 227"},"PeriodicalIF":1.3,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12647-026-00891-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of Uncertainty of an Ozone Analyzer Installed at an Ambient Air Quality Station in Delhi","authors":"Priya Dwivedi,&nbsp;Radhakrishnan S. R.","doi":"10.1007/s12647-026-00906-5","DOIUrl":"10.1007/s12647-026-00906-5","url":null,"abstract":"<div><p>Accurate and traceable measurement of ambient ozone is essential for national air quality monitoring and regulatory decision-making. This study presents a comprehensive evaluation of measurement uncertainty associated with ozone analyzer deployed at a Continuous Ambient Air Quality Monitoring Station (CAAQMS) in Delhi, India. The uncertainty budget was developed in accordance with the Guide to the Expression of Uncertainty in Measurement (GUM: JCGM 100:2008) using experimental observations at zero and span concentration levels. Short-term uncertainty was quantified using Allan deviation analysis, while long-term components arising from temperature, pressure, and instrumental drift were systematically evaluated. Results indicate that gas cell pressure variations represent the dominant contributor to total uncertainty, followed by temperature effects and photometric noise. The combined standard uncertainty was estimated as 2.27 nmol/mol, with an expanded uncertainty (k = 2) of 4.54 nmol/mol, satisfying international regulatory and metrological requirements. The study establishes a metrological benchmark for ozone measurements within the Indian ambient air quality-monitoring framework.</p></div>","PeriodicalId":689,"journal":{"name":"MAPAN","volume":"41 2","pages":"531 - 537"},"PeriodicalIF":1.3,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148061039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of Factors Influencing PM2.5 Using Sentinel-5P and CPCB Data by Machine Learning Techniques: Case Study of Gurugram City (2019–2023)","authors":"Shilpa Mahajan,&nbsp;Pankaj Rathi,&nbsp;Duiena Rai,&nbsp;Tripti Sharma,&nbsp;Avi Aneja,&nbsp;Avni Jettley","doi":"10.1007/s12647-026-00907-4","DOIUrl":"10.1007/s12647-026-00907-4","url":null,"abstract":"<div><p>PM_2.5 particulates are major contributing factors that pose a serious threat to public health, particularly in urban cities like Gurugram, India. This study investigates the spatiotemporal variations of PM_2.5 concentration in Gurugram from 2019 to 2023 by integrating satellite and surface-level data. Meteorological and ground-level air quality data were collected through the Central Pollution Control Board, and spatial patterns of pollution were collected using satellite data from Google Earth Engine. Supervised Machine learning algorithms were then used to predict PM_2.5 concentrations and identify the key parameters influencing pollution. Among the evaluated models, the Random Forest algorithm demonstrated superior performance, achieving a coefficient of determination (R²) of 0.912, a mean absolute error of 2.946, a root mean square error of 5.013, and a mean square error of 25.13 Analysis has revealed that ground-based predictors exhibited stronger linear association with PM_2.5, whereas satellite-derived predictors captured broader regional trends. Strict tests for accuracy and precision in satellite-retrieved data were performed using comparative studies with ground measurement datasets. Temporal analysis indicated strong seasonal variation with the elevated PM_2.5 recorded during the winter months, whereas spatial analysis using satellite data revealed that the densely populated areas and transportation-dominated zones have high levels of pollutants. The findings demonstrate that the combination of satellite-based atmospheric indicators with ground measurements increases the spatiotemporal characterisations of air quality in cities.</p></div>","PeriodicalId":689,"journal":{"name":"MAPAN","volume":"41 1","pages":"285 - 300"},"PeriodicalIF":1.3,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case studies on the Physical and Optical Properties of Clouds Observed Over a Western Himalayan Site","authors":"Vasundhara Sharma,&nbsp;Shishir Kumar Singh,&nbsp;Devesh Kumar Shukla,&nbsp;Vijender Kumar Bambal,&nbsp;Ashish Ranjan,&nbsp;Radhakrishnan Soman Radha","doi":"10.1007/s12647-026-00902-9","DOIUrl":"10.1007/s12647-026-00902-9","url":null,"abstract":"<div><p>Low-altitude clouds play a crucial role in regional weather dynamics and global climate feedbacks, yet ground-based observations in mountainous regions like the Western Himalayas remain limited. This study analyzes the physical and optical properties of low-altitude rain-bearing clouds over Palampur, India, using Raman lidar measurements from two case studies: June 1, 2023 and May 16, 2017. Key parameters, including backscatter coefficient, depolarization ratio, cloud optical depth (COD), geometrical thickness, cloud-to-subcloud ratio (CSR), and turbulence (refractive index parameter, C_n²), were derived to characterize cloud evolution under western disturbances and orographic influences. Results show cloud base heights ranging from 2145 to 5362 m, with average thicknesses of 508 m (Case Study 1) and 468 m (Case study 2), and COD values up to 2.18, indicating predominantly optically thick mixed-phase clouds. Depolarization ratios (0.10–0.60) revealed transitions from water droplets to ice crystals, while positive COD-CSR correlations (<i>r</i> = 0.72 in Case Study 2) highlighted enhanced aerosol-cloud interactions. Turbulence increased during cloud growth, peaking before precipitation. These findings align with CALIPSO satellite literature, validating Raman lidar's high-resolution insights in complex terrain. This work addresses a data gap in Himalayan cloud climatology, contributing to improved regional weather forecasting and climate modeling.</p></div>","PeriodicalId":689,"journal":{"name":"MAPAN","volume":"41 2","pages":"515 - 529"},"PeriodicalIF":1.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148061015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HIAPLLM: IoT-Enabled Hybrid Edge–Cloud LLM for Real-Time, Privacy-Preserving Air Quality Advisories in India","authors":"Partha Pratim Ray,&nbsp;Mohan Pratap Pradhan","doi":"10.1007/s12647-026-00908-3","DOIUrl":"10.1007/s12647-026-00908-3","url":null,"abstract":"<div><p>We present HIAPLLM, a privacy-preserving hybrid edge–cloud framework for real-time air-quality advisories tailored to the Indian context using Large Language Models (LLMs). At the network edge, a Raspberry Pi 4 B hosts a Gradio/Folium interface enabling users to input any Indian geocoordinates. Upon each request, the Pi issues a 5 s-timeout call to the Open-Meteo application programming interface (API), retrieving <span>(hbox {PM}_{2.5})</span> (5.2–82.9 <span>({upmu }{textrm{g}}/{textrm{m}}^{3})</span>), <span>(hbox {PM}_{10})</span> (8.6–172.8 <span>({upmu }{textrm{g}}/{textrm{m}}^{3})</span>), CO (0.10–0.80 <span>(hbox {mg/m}^{3})</span>), <span>(hbox {NO}_{2})</span> (3.6–46.5 <span>({upmu }{textrm{g}}/{textrm{m}}^{3})</span>), <span>(hbox {SO}_{2})</span> (0.3–40.7 <span>({upmu }{textrm{g}}/{textrm{m}}^{3})</span>), <span>(hbox {O}_{3})</span> (35–160 <span>({upmu }{textrm{g}}/{textrm{m}}^{3})</span>), aerosol optical depth (AOD: 0.14–1.10), dust loading (0–238 units), and <span>(hbox {CH}_{4})</span> (1 316–1 614 ppm). These concentrations are mapped via piecewise linear breakpoints into air quality index (AQI) sub-indices and qualitative bands. The Pi then constructs a structured LLM prompt (e.g., “<span>(hbox {PM}_{2.5})</span> = 82.9 <span>({upmu }{textrm{g}}/{textrm{m}}^{3})</span>; Category: Poor”) and forwards it over LAN to a laptop running ten quantized LLMs (0.6–7 B parameters) under the Ollama engine, eliminating external cloud dependency and preserving raw location privacy. We profile model load times (0.80–2.46 s) and inference latencies (6.26–94.05 s), and find a strong inverse relationship between token throughput and total latency (slope − 0.516 s per token/s, <span>(R^2=0.552)</span>). Multivariate analyses (Principal Component Analysis (PCA) capturing 42 %/27 % variance on PC1/PC2; hierarchical clustering) and anomaly detection (Mahalanobis: Kota; Isolation Forest: Delhi, Ludhiana; Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clusters: Hyderabad–Udaipur, Jhansi–Jamshedpur, Belagavi–Nellore) reveal distinct environmental–computational patterns across Indian cities. Variance Inflation Factors (<span>(hbox {PM}_{10})</span>: <span>(8.75times 10^3)</span>, dust: <span>(3.66times 10^3)</span>, total_duration_s: <span>(3.84times 10^{10})</span>) expose extreme collinearity, addressed via dimension reduction. HIAPLLM’s runtime of <span>(O(N+P)+T_{textrm{LLM}})</span> and space <span>(O(N+P+m))</span> ensures scalable, transparent, and low-cost deployment suitable for India’s connectivity-constrained, privacy-sensitive settings. Code and data are available at https://github.com/ParthaPRay/Air_Pollution_LLM_Advisor.</p></div>","PeriodicalId":689,"journal":{"name":"MAPAN","volume":"41 2","pages":"485 - 513"},"PeriodicalIF":1.3,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148061154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Validation of Interpolation and Function Approximation Techniques for Error Compensation in Angle Measurement Systems","authors":"Donatas Gurauskis,&nbsp;Albinas Kasparaitis","doi":"10.1007/s12647-026-00901-w","DOIUrl":"10.1007/s12647-026-00901-w","url":null,"abstract":"<div><p>Industrial rotary angle measurement systems often employ mathematical modelling techniques for error compensation. In practice, choosing the most suitable method for systematic error modelling is important, because it directly influences overall accuracy and reliability of the system. However, comparative evaluations of different approaches remain limited. This study systematically evaluates six angular error compensation methods using a comprehensive, uncertainty-aware assessment framework. Angular deviation data was obtained from repeated cross-calibration measurements of an industrial rotary angle measurement system using a 36-sided polygon and an autocollimator. Three interpolation methods (linear, nearest-neighbor, cubic spline) and three approximation methods (polynomial, Fourier series, sum-of-sines) were evaluated using validation RMSE, information criteria, cross-validation, bootstrap robustness, Monte Carlo uncertainty propagation, and spectral analysis. Results show that linear interpolation achieves the lowest validation RMSE (0.648″) with good robustness and implementation simplicity. Among approximation methods, a fourth-degree polynomial provides the best robustness, while a three-term sum-of-sines model achieves comparable accuracy to a four-harmonic Fourier series using 56% fewer parameters and superior data robustness. Moreover, the study reveals a critical overfitting paradox: models exhibiting excellent noise smoothing can be catastrophically sensitive to calibration data selection. These findings demonstrate that bootstrap and Monte Carlo analyses quantify complementary robustness properties and are essential for reliable model selection. A comprehensive assessment framework is therefore required to ensure stable and physically meaningful error compensation in precision measurement systems.</p></div>","PeriodicalId":689,"journal":{"name":"MAPAN","volume":"41 1","pages":"229 - 252"},"PeriodicalIF":1.3,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}