Measurement Data Research Articles

Calculation of Tool Offset and Tool Radius Errors Based on On-Machine Measurement and Least Squares Method in Ultra-Precision Diamond Turning

Metal mirrors will be widely used in the coming decades. Therefore, as one of the enabling technologies for metal optical freeform surface manufacturing, ultra-precision (UP) diamond turning error compensation has become a research hotspot. However, for the tool offset error and tool radius error, which are the main errors in UP diamond turning, no precise and efficient calculation method has been found in the literature. In this study, a more precise and efficient algorithm was developed and validated in three ways using on-machine measurement data and profilometer measurement data. After one compensation, the tool offset error can be reduced to below 0.1 μm, and the tool radius error can be reduced to below 1 micrometer, which will significantly improve the UP turning accuracy and efficiency of optical parts.

User Preference Maps: Quantifying the Built Environment

The built environment in which we live holds the potential to provide life experiences that allow pedestrians to observe, feel, learn, and grow through their surroundings in everyday urban spaces. If a city offers opportunities for careful observation and exploration according to users’ preferences, it will become more appealing to many people. This study selected Midtown, New York, as the research site and collected a total of seven datasets based on 30 intersections in the area. The data, categorized into three main areas—activity, comfort, and natural elements—were evaluated, visualized, and restructured using a path exploration algorithm to produce a final user-based map. For this, 3D modeling software Rhino version 7, visual programming tool Grasshopper, and Grasshopper verion 2023 plugin programs were used. The final result included 3D route information, quantitative measurement data, and multidimensional visual materials. This approach presents an alternative to traditional route navigation based on uniform criteria and, through data-driven design, is believed to ultimately enhance walkability, activate urban spaces, and contribute to the development of sustainable cities. The scope of related research can further expand as the targets, duration, and methods of data collection continue to evolve and as case studies in various cities increase.

The Synthesis of MOFs of ZnImBImZnO through Hydrothermal and Solvothermal Methods

Metal-organic frameworks (MOFs) are a promising class of materials that have a wide range of applications, from gas storage to catalysis. The synthesis of MOFs from the ZnImBImZnO framework has been carried out using hydrothermal and solvothermal methods. By using different metal-ligand molar ratios and solvents, we have discovered that MOFs can be formed both in DMF and water solvents. The products obtained were studied using various techniques, including SEM, PSA, and FTIR, to confirm their formation Our research has shown that MOFs synthesized with different concentrations and solvents show different size distributions. This suggests that the choice of solvent and concentration can be used to control the size and morphology of the MOFs. Furthermore, we have found that the formation of the MOFs framework also directs the morphology of the Zn(II)-imidazole complex and ZnO NFs. Our results provide important insights into the synthesis and properties of MOFs. Moreover, the contact angle measurement data for the ZnImBImZnO framework indicates that it is unequivocally hydrophilic. By understanding the factors that influence the properties of MOFs, we can design new materials with specific properties tailored to different applications. This exciting research area has the potential to revolutionize many fields, from energy storage to drug delivery. Our results provide important insights into the properties of MOFs and pave the way for developing new materials with tailored properties.

Accelerating Deep Learning in Radar Systems: A Simulation Framework for 60 GHz Indoor Radar

FMCW radar systems are increasingly used in diverse applications, and emerging technologies like JCAS offer new opportunities. However, machine learning for radar faces challenges due to limited application-specific datasets, often requiring advanced simulations to supplement real-world data. This paper presents a setup for generating synthetic radar data for indoor environments, evaluated using CNNs. The setup involves comprehensive modeling, including far-field antenna simulations, variations in human radar cross-section, and detailed representations of indoor environments with their corresponding propagation channel properties. These synthetic data are used to train CNNs, and their performance is assessed on real measurement data. The results demonstrate that CNNs trained on synthetic data can perform well when tested on real measurement data. Specifically, the models trained with synthetic data showed performance comparable to models trained with real measurement data, which required a minimum of 300 samples to reach similar levels of accuracy. This result demonstrates that synthetic data can effectively train neural networks, providing an alternative to real measurement data, particularly when collecting sufficient real-world samples is difficult or costly. This approach significantly reduces the time required for generating datasets, and the ability to quickly label data in simulations simplifies and accelerates post-processing. Additionally, the generated datasets can be made more heterogeneous by introducing varying signal conditions, enhancing the diversity and robustness of the training data.

Coral skeletal proxy records database for the Great Barrier Reef, Australia

Abstract. The Great Barrier Reef (GBR), Australia, has a long history of palaeoenvironmental coral research. However, it can be logistically difficult to find the relevant research and records, which are often unpublished or exist as “grey literature”. This hinders researchers' abilities to efficiently assess the current state of coral core studies on the GBR and thus identify any key knowledge gaps. This study presents the Great Barrier Reef Coral Skeletal Records Database (GBRCD), which compiles 208 records from coral skeletal research conducted since the early 1990s. The database includes records from the Holocene, from ∼8000 years ago, to the present day; records are from the northern, central, and southern GBR from inshore and offshore locations. Massive Porites spp. coral records comprise the majority (92.5 %) of the database, and the remaining records are from Acropora, Isopora, or Cyphastrea spp. The database includes 78 variables, with Sr/Ca, U/Ca, and Ba/Ca being the most frequently measured. Most records measure data over 10 or more years and are at monthly or lower resolution. The GBRCD is machine readable and easily searchable so that users can find records relevant to their research, e.g. by filtering for site name, time period, or coral type. It is publicly available as comma-separated values (CSV) data and metadata files with entries linked by the unique record ID and as Linked Paleo Data (LiPD) files. The GBRCD is publicly available from the NOAA National Center for Environmental Information World Data Service for Paleoclimatology at https://doi.org/10.25921/hqxk-8h74 (Arzey et al., 2024). The intention is to update the GBRCD annually, depending on the availability of relevant new GBR records or submission of legacy records to the GBRCD for archiving. The collection and curation of existing GBR coral research data provide researchers with the ability to analyse common proxies such as Sr/Ca across multiple locations and/or examine regional to reef-scale trends. The database is also suitable for multi-proxy comparisons and combination or composite analyses to determine overarching changes recorded by the proxies. This database represents the first comprehensive compilation of coral records from the GBR. It enables the investigation of multiple environmental factors via various proxy systems for the GBR, northeastern Australia, and potentially the broader Indo-Pacific region.

Adaptive Control for Hydronic Radiant Heating System Using Occupant Behaviors in Residential Building

This study proposes an occupant-centric control strategy for residential heating systems, aiming to enhance thermal comfort and reduce energy consumption. A sensor station utilizing a frequency-modulated continuous wave radar sensor was developed to detect occupancy and infer activities within residential spaces. By analyzing field measurement data, schedules for occupancy and activities were established. These schedules were then used to implement a variable control strategy for the hydronic radiant heating system, adjusting its operating characteristics based on the identified activities. The proposed control strategy, which includes resetting the indoor set temperature during unoccupied periods and adjusting it during sleep to account for changes in metabolic rate and clothing insulation, resulted in significant energy savings. Compared to continuous operation, the hydronic radiant heating system’s energy consumption was reduced by approximately 21% on peak load days and up to 34% over three winter months. This study demonstrates the potential of occupant-centric control for achieving substantial energy savings in residential buildings while maintaining occupant thermal comfort.

Investigating uncertainties in air quality models used in GMAP/SIJAQ 2021 field campaign: General performance of different models and ensemble results

The international field campaign, GMAP/SIJAQ 2021, was conducted in Korea from October 18th to November 25th to enhance the performance and validation of the Geostationary Environment Monitoring Spectrometer (GEMS) products algorithm and obtain a better understanding of the current air pollution status of the Korean Peninsula. Five chemical transport models (CTMs), including CMAQ, CMAQ-GIST, CAMx, WRF-Chem, and WRF GEOS-Chem, were utilized during the campaign to assist in organizing the observation plan and identifying changes in pollutant concentrations and their spatiotemporal distribution in Korea following the Korea–United States Air Quality (KORUS-AQ) 2016. In this study, we evaluated the forecasting performance, strengths, and limitations of these five CTMs and their ensemble in simulating air quality. Intensive measurement data and intercomparisons were employed to explain discrepancies between observed and simulated results. A comparison of the CTM ensemble results for PM2.5 and various gaseous pollutants between the current GMAP/SIJAQ 2021 and previous KORUS-AQ 2016 campaigns showed the R-value for the total mass PM2.5 concentration increased from 0.88 to 0.94. This improvement is related to CTM updates, including the emission inventory and better reproductions of the concentrations of gaseous species. However, the models consistently underestimated carbon monoxide (CO) concentrations, similar to the results from KORUS-AQ. This finding still suggests a further challenge that requires consideration of missing anthropogenic sources. The results of the ensemble model agreed well with the chemical composition of PM2.5 observed at the intensive monitoring station. However, for NO3− and NH4+, discrepancies were primarily due to inaccuracies in the meteorological inputs, such as precipitation, relative humidity (RH), and nighttime planetary boundary layer height (PBLH) in the CTMs. Hence, all models overestimated the concentration of elemental carbon (EC), therefore, it is necessary to revise EC emissions in the SIJAQv2 inventory, as these apply to unusual levels recorded in Seoul during the reference year of 2018.

Research on wireless channel model based on improved ray tracing algorithm that considers multiple diffuse scattering and employs pyramid-shaped ray tubes as the carrier

This paper extensively utilizes fine three dimensional environmental data obtained from laser point clouds. Based on theories such as geometrical optics and effective roughness theory, a deterministic wireless channel model is established, which integrates higher-order diffuse scattering. This model is referred to as the ray tracing fusion with higher-order diffuse scattering model. To expedite the collision calculation between rays and the scene, this paper introduces a combined approach of voxelization and signed distance field, resulting in a remarkable 16-fold improvement in computational speed. Moreover, aiming to balance accuracy and efficiency, the paper systematically analyzes the optimization computation parameters of the model. Finally, the proposed model is validated using measurement data in the frequency range of 1 GHz to 6 GHz in mountainous terrain. The results indicate that the predicted outcomes of the proposed model have an accuracy within 6 dB compared to the measurement results, and are superior to ITU-R P.1546, which is an international standard recommended by the International Telecommunication Union for modeling electromagnetic wave propagation in undulating terrain. This provides necessary technical support for network planning and optimization.

Potential use of IoT Distance Measurement Tool in Boule Sports

In Petanque, each player aims to throw the boule closer to the jack. The closest boule to the jack among players will score the point. Currently, the distance of the boule to the jack is still measured using manual measurement tools such as measuring tape, string, and calipers. The manual measurement method is considered time-consuming and prone to inconsistent reading, which the ordinary referees and players conduct. A steady hand is required to hold the tape at two ends while squatting or kneeling. The technique of reading the measurement is also important to determine the accuracy of the length. This project aims to design and develop a prototype device that can measure the distance between jack and boule using a microcontroller and ultrasonic sensor technology. The device is expected to provide an instant measurement of the distance between the jack and the boule. The measurement data can be displayed on the mobile device to ease the user to view the result. This prototype device also counts the score points and determines the winner.

Measurements and medication data are indispensable for population blood pressure monitoring

Abstract Background The National Study on Health of Older People in Germany “Gesundheit 65+” included blood pressure (BP) measurements at home by a study nurse and classification of all current medication. Methods Nationally representative examination survey of the population aged 65 years and older residing in Germany using two-stage population registry sampling from 128 sample points, with a standardized examination at home from June 2022 to April 2023. Three standardized blood pressure measurements at two-minute intervals were taken after 5 minutes rest with an oscillometric device (Mobil-O-Graph) using one of five cuffs according to upper arm circumference. The mean of the second and third measurement was used for analysis. Hypertension was defined as mean systolic BP (SBP) at or above 140 mmHg, or mean diastolic BP (DBP) at or above 90 mmHg, or the use of antihypertensives (antihypertensives, diuretics, beta-blockers, calcium-channel blockers, ACEI and ARB) in participants with self-reported hypertension diagnosis. Provisional weights account for unequal sampling and participation probabilities. Results The “Gesundheit 65+” examination part included 1,493 participants of whom 1,475 (98.8%) had successful BP measurements (763 men and 712 women, aged 66 to 101 years). Based on self-reported diagnoses alone, the prevalence of hypertension would be 58.3% [95% CI 53.2-63.3] in men and 59.5% [53.8-64.9] in women. However, overall hypertension prevalence was estimated at 70.4% [65.8-74.7] in men and 72.3% [66.9-77.1] in women based on the study definition which combines self-reported diagnoses, standardized BP measurements and medication. Conclusions The prevalence of hypertension is greatly underestimated in the absence of examination survey data with standardized measurements and recording of medication use. In addition, measurement and medication data are needed for monitoring the hypertension cascade, i.e. awareness, treatment and control of hypertension. Key messages • Hypertension prevalence is underestimated in the absence of health examination surveys with measurements and medication data. • The hypertension cascade, i.e. assessment of awareness, treatment and control of hypertension needs population surveys with measurements and medication assessment.

Displacement prediction of tunnel entrance slope based on LSSVM and bacterial foraging optimization algorithm

In order to realize the effective prediction of landslide risk in the tunnel entrance area, an multivariate time series model is established on the basis of the traditional model, taking temperature and rainfall factors as additional input indicators. Bacterial foraging optimization algorithm (BFOA) is used to search the global optimal solution of the key parameters \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma$$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma^{2}$$\\end{document} of least squares support vector machine (LSSVM) to improve its regression accuracy, and the evolved LSSVM is used to describe the aforementioned multivariate time series model. At the same time, a remote real-time internet of things (IoT) monitoring system for the tunnel entrance section, including monitoring indicators such as surface subsidence, temperature, and rainfall, has also been designed and implemented, providing a stable and accurate data source for the realization of this prediction model. Based on the engineering measurement data, the accuracy of the established model is checked and analyzed, the optimal value of historical data amount is determined to be 5 days, and the optimal value of prediction step is 1 day. The research results are applied in the construction of Wendong tunnel of Molin expressway, Yunnan, China. Practice shows that the prediction results of the multivariate time series model established in this study is accurate. This method can realize the prediction and early warning of slope risk, which provides a effective technical means for risk control of tunnel portal section.

Elucidation of spacecraft surface wear phenomena in the plasma environment emitted by electric propulsion vehicles based on spacecraft charging analysis

Professor Takanobu Muranaka, Chukyu University, Japan, is part of a collaborative research effort to investigate and elucidate surface wear phenomena in electric propulsion spacecraft vehicles. He and his team are exploring how injected plasma emitted by these vehicles interacts with the spacecraft, leading to potential electrical and mechanical issues. For example, the ions and electrons in the plasma can collide with the spacecraft, causing wear and tear. This can compromise the spacecraftâ–™s durability and lifespan. In order to analyse these interactions, Muranaka and the team are utilising numerical calculations. To overcome limitations associated with the analysis of spacecraft systems and plasma in space, the researchers perform analyses of objects under predetermined conditions without consideration of space or timescales. Although it isnâ–™t possible to fully replicate space conditions in ground-based vacuum chambers, in their analyses the researchers are able to obtain important raw measurement data on physical phenomena that have not been modelled, such as parameter measurements of electrically propelled plasma plumes. Through its research, the team hopes to contribute to spacecraft design by informing design parameters such as the spacecraft shape and thruster parameters. Ultimately, the researchers are expanding and verifying numerical models and developing advanced plasma interference analysis technologies that will prove invaluable to spacecraft design. The researchers’ investigations on the Hayabusa2 spacecraft’s side-cathode ion thruster system have yielded exciting results on the ion energy distribution function (IEDF) of backflowing ions at multiple points around this thruster, expanding their findings on the significance of thruster reference potential on ion energy distribution, which is key to understanding spacecraft surface erosion.

Remote sensing and machine learning algorithms to predict soil salinity in southern Kazakhstan

Salinization and land degradation are significant challenges in the southern regions of Kazakhstan. These issues arise due to climate change, unequal water resource distribution, and human impact. The primary concern revolves around water resources, which are influenced by the area’s trans boundary flow of major rivers. The low level of water and food security has pushed the development of new approaches based on remote sensing monitoring and geographic information systems (GIS) to provide solutions for soil salinity. The research aims to focus on utilizing high-resolution radar images. This data type is effective for cloudy weather and can be useful for continued monitoring of some areas. Machine learning methods can solve the problem of automatic mapping of agricultural land salinity in Kazakhstan’s southern regions. The precise mapping of the salinity area helps prevent or decrease salinity’s impact on agriculture. The experiment realized that complex models such as LightGBM do not have significant accuracy performance over simple models on a small dataset compared with Ridge regression. The results allow us to recommend an approach for further improvement with ground-based measurement data and other deep-learning methods for mapping the salinity of agricultural lands.

Prevalence and treatment of chronic musculoskeletal pain focused on service gaps: a comparative analysis by age group and body part.

Occupational health, grounded in occupational medicine, aims to enhance the well-being and labor productivity of the working population. Within this realm, there has been growing concern over the increasing instances of labor loss due to diminished physical capacity and the rise in musculoskeletal disorders. Chronic pain is also associated with musculoskeletal disorders. Another pressing issue related to chronic musculoskeletal pain was needed assistance services are underutilized, is referred to as a "service gap". Understanding trends based on age and affected regions of the body is indispensable for developing strategies to address chronic musculoskeletal pain in workers. This study aimed to elucidate age-specific trends in the prevalence and number of pain sites in chronic musculoskeletal pain as well as the patterns of treatment during chronic musculoskeletal pain, categorized by age and affected site. This study was conducted in December 2022 in contract to Cross Marketing Inc. The survey began on December 12, 2022 and ended on December 15, 2022. An online survey was administered to 1,946 participants (973 women and 973 men), and responses were collected, ensuring a roughly equal distribution of samples among men and women across six age groups ranging from their 20 to 70 s. The survey inquired about the presence of chronic pain in each of the eight body parts and the adoption of five different treatments when experiencing pain. Statistical analysis was performed using the chi-square test, with the measurement data categorized by age group and body part. The results indicated a significantly higher prevalence of chronic pain at three or more sites among individuals in their 30 s: the prevalence of pain at each of the eight body sites varied with age. Approximately half of the participants did not engage in specific treatments during episodes of chronic pain. The presence or absence of treatment showed no significant differences according to affected site or age group. This study revealed age- and body part-related relationships with chronic musculoskeletal pain in middle-aged patients and highlighted healthcare service gaps in its management in Japan.

Impact of excavation footage on vertical shaft lining deformation and surrounding rock stress in Xiyu conglomerate

The Xiyu conglomerate, which is extensively distributed in Xinjiang, China, presents challenges for vertical shaft construction owing to its poor cementation and low strength. The selection of an appropriate footage length is paramount for ensuring safe construction. Based on the shaft pipeline construction project of the Xinlongkou Hydropower Station on the Kuitun River, this study investigated the impact of footage length on the stability of vertical shafts within the Xiyu conglomerate strata using finite element simulation and measurement data to analyze the lining displacement and change in stress in the surrounding rock. The results show that the excavation footage has little effect on the lining’s horizontal displacement during vertical shaft excavation. A mathematical model was developed to describe how the vertical displacement of the lining varies with the excavation footage and vertical shaft depth. Below a critical threshold, the stress in the surrounding rock cannot be effectively alleviated, leading to a concentration of stress; above that critical threshold, an excessively large excavation increases the load on the surrounding rock. Based on the simulation results and factors such as economic benefits, construction speed, and safety, an excavation footage of 4 m is judged to be appropriate.

Research on the Green Construction Technology of Stilt Houses Based on the Climate Adaptation of Transitional Seasons

Stilt houses are extremely adaptable to terrain and climate. However, current indoor thermal environment research in transitional seasons is not prominent. Therefore, in this research, a typical stilt house in southwest China was chosen as the research object and analyzed by combining Climate Consultant climate analysis software and field measurement data. The results showed that the indoor thermal stability of a stilt house was excellent during the transition season, and the attic had the most obvious climate regulation, with the maximum temperature difference between indoors and outdoors being 9 °C when the outdoor temperature was the highest. The difference between the mean radiant temperature and the average air temperature was only 0.04 °C, and the radiant effect of the enclosure on the interior was small. The indoor relative humidity ranged from 63.2% to 85.1%, showing high relative humidity, but the fluctuation was relatively stable. Stilt floors did not play a significant role in climate regulation during the transition season, and the semi-open space structure was more prone to moisture accumulation when the outdoor humidity was high. Regarding practical application, the climate adaptation strategies of shading, cooling, and dehumidification were applied in the transition season, but dehumidification was ineffective.

Dynamic Response of a Rotor Supported on Hybrid Bearings in Air, Water, and Liquid Nitrogen: Measurements and Comparisons to Predictions

Abstract Modern liquid rocket engine turbopumps implement hybrid bearings, which combine hydrostatic and hydrodynamic fluid film principles, in compact and lightweight units known for their superior reusability, durability, and reliability. This study aims to provide extensive and reliable test data for the purpose of anchoring and benchmarking predictive tools for these bearings. This paper provides comprehensive dynamic response measurements of a rotor weighing 10.40 kg and approximately 60 mm in diameter at the bearing locations. The test rotor is supported on two hybrid journal bearings with a bearing length to bearing diameter ratio of approximately 0.42 and a bearing radial clearance to rotor radius ratio of around 0.0017. The bearings are tested with air, water, and liquid nitrogen, serving as surrogate test fluids for common propellants in liquid rocket engines. Rotor run-up and speed coast-down tests are conducted to measure shaft motion responses. The results clearly demonstrate that the rotordynamic characteristics of the test rotor supported on the hybrid journal bearings largely rely on properties of the test fluids and their feeding conditions. Notably, when air is pressurized into the test bearings, the shaft motion responses differ markedly from those observed with water and liquid nitrogen, primarily due to significantly lower stiffness and damping coefficients. Furthermore, rotor speed coast-down tests for each test fluid exhibit notable differences in coast-down speed over time characteristics, depending on the test fluid properties. The predicted rotor imbalance responses exhibit excellent correlation with the measurement data. The steady increase of demand and growth of the fluid film bearing technology for reusable rocket engines require accurate bearing design tools, the extensive component testing, and the implementation of the technology. The validated and developed bearing predictive models from previous research demonstrate well the characteristics of bearings under various operating fluids conditions. The findings and database presented in this work contribute not only to comprehending the performance of hybrid bearings but also to understanding and enhancing the dynamic characteristics of rotor systems supported on hybrid bearings.

HOW DOES THE DIGITAL ECONOMY AFFECT ENERGY EFFICIENCY?

As a new way of promoting high-quality economic development, whether the digital economy can enhance energy use efficiency by improving resource allocation is of great practical significance to global energy conservation and emission reduction efforts. This research employs principal component analysis and slacks-based measure data envelopment analysis in a case study of 30 provinces in China between 2014 and 2020 to evaluate the digital economy and energy efficiency. The analysis explores how the digital economy impacts energy efficiency, considering direct impacts, mediating effects, and their nonlinear relationship. The findings indicate a declining trend in China’s energy efficiency before it starts to moderate. The digital economy can boost energy efficiency by encouraging industry upgrades and reducing labor mismatches. The threshold regression model reveals that when the capital mismatch level exceeds 0.8871, the digital economy adds considerably to energy efficiency. The study’s findings confirm the linear and nonlinear energy efficiency impact trajectories of the digital economy, offering useful references for sustainable development policymaking.

Variations of the zero degrees isotherm and environmental lapse rate recorded with ADSB and Mode S EHS messages

This study focuses on analyzing the altitude of the zero degrees isotherm and variations in the environmental lapse rate (ELR) using the publicly available data collected from Automatic Dependent Surveillance–Broadcast (ADS-B) and Mode S Enhanced Surveillance (EHS) messages emitted by airplanes over a five-month period in 2021. The data was gathered using a professional receiver stationed in Bucharest (Romania). The aviation messages were decoded and the air temperature and pressure were determined, at the location of the airplane. The method has the advantage of the continuous messages that are emitted by the aircrafts during flight that allow instantaneous determination of the meteorological parameters at no additional costs. It can also be extended to permit almost real time maps of the ELR. When data was analyzed and a standard ELR value of 6.5 K/km is employed it was observed that the mean altitude of the 0 degrees isotherm exhibits a seasonal increase during the summer months, with an average altitude of 2874.2 m. The highest recorded altitude of the 0 degrees isotherm was found to be 5346.8 m, near Alexandria city (Romania), on 22.07.2021. Using a standard Least Mean Square algorithm alongside the International Standard Atmosphere pressure formula, the ELR values were calculated from pressure measurements data. The resulting mean ELR for the five-month period was determined to be 5.1331 K/km, slightly lower than the standard value.

Lipid and brain volumetric measures in multiple sclerosis patients: findings from a large observational study.

This study aimed to investigate relationships between cholesterol profile, brain volumetric MRI, and clinical measures in a large observational cohort of multiple sclerosis (MS) patients. We included 1.505 patients with 4.966 time points including complete lipid, clinical, and imaging data. The time among lipid, brain MRI and clinical measures was under 90 days. Cross-sectional statistical analysis at baseline was performed using an adjusted linear regression and analysis of longitudinal lipid and MRI measures data was performed using adjusted linear mixed models. We found associations between higher high-density lipoprotein cholesterol (HDL-C) and lower brain parenchymal fraction (BPF) at cross-sectional analysis at baseline (B = -0.43, CI 95%: -0.73, -0.12, p = 0.005), as well as in longitudinal analysis over follow-up (B = -0.32 ± 0.072, χ2 = 36.6; p = < 0.001). Higher HDL-C was also associated with higher T2-lesion volume in longitudinal analysis (B = 0.11 ± 0.023; χ2 = 23.04; p = < 0.001). We observed a weak negative association between low-density lipoprotein cholesterol (LDL-C) levels and BPF at baseline (B = -0.26, CI 95%: -0.4, -0.11, p = < 0.001) as well as in longitudinal analysis (B = -0.06 ± 0.03, χ2 = 4.46; p = 0.03). T2-LV did not show an association with LDL-C. We did not find any association between lipid measures and disability. The effect of lipid levels on MRI measures and disability was minimal (Cohen f2 < 0.02). Our results contradict the previously described exclusively positive effect of HDL-C on brain atrophy in patients with MS. Higher LDL-C was weakly associated with higher brain atrophy but not with higher lesion burden.