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Due to the lack of the corresponding calibration device and national calibration specification, center distance calipers cannot be effectively calibrated, which can lead to inaccuracy of the center distance calipers. To standardize the metrological calibration of center distance calipers, this article designs an adjustable center distance caliper calibration device, which is mainly composed of standard gauge blocks and specially designed accessories. The measurement range of the calibration device is (0∼2, 000) mm, with the maximum permissive error of ±(0.80+1.6×10 −5 L ) μm. Experimental results and uncertainty analysis show that the designed calibration device can completely meet the need of calibrating indication error of center distance calipers. The system is stable in performance and strong in repeated measurement ability, which is of great significance for the effective calibration and improvement research of center distance calipers.

We present a new algorithm which detects the maximal possible number of matched disjoint pairs satisfying a given caliper when a bipartite matching is done with respect to a scalar index (e.g., propensity score), and constructs a corresponding matching. Variable width calipers are compatible with the technique, provided that the width of the caliper is a Lipschitz function of the index. If the observations are ordered with respect to the index then the matching needs O(N) operations, where N is the total number of subjects to be matched. The case of 1-to-n matching is also considered. We offer also a new fast algorithm for optimal complete one-to-one matching on a scalar index when the treatment and control groups are of the same size. This allows us to improve greedy nearest neighbor matching on a scalar index.

Background The global rise in allergic diseases has made accurate identification of atopy increasingly important. The skin prick test is the most common diagnostic tool, and both its correct performance and standardized interpretation are essential for reliability. Reliability, defined as the consistency of measurements across different observers and instruments, was evaluated by comparing ruler- and caliper-based wheal measurements. This study aimed to assess the consistency and reliability of skin prick test measurements performed with two different instruments and by two different nurses in a pediatric allergy clinic. Methods This prospective study included 100 children aged 6–18 years at Erciyes University Faculty of Medicine. For each participant, skin prick test wheal diameters were measured using both a standard ruler and a digital caliper, each by two different nurses. Measurement times were recorded by an independent observer using a stopwatch. Consistency between observers and instruments was evaluated by comparing ruler- and caliper-based results. Results Measurements obtained with the digital caliper showed stronger agreement between the two nurses than those taken with the ruler. The mean inter-observer difference for caliper-based measurements was 0.03 mm ( p  > 0.05), whereas ruler-based measurements differed by 0.22 mm between the two nurses ( p  < 0.05), indicating reduced inter-observer variability. The digital caliper provided more precise, consistent, and reliable readings of wheal size, underscoring its advantage in standardized test evaluation. Conclusion Measurement techniques and instrument types significantly affect the accuracy and sensitivity of skin prick test interpretation. Despite being a reliable method for confirming IgE-mediated sensitization, manual reading introduces variability. Using a digital caliper minimizes potential errors and enhances the precision and reproducibility of results, contributing to more standardized and reliable allergy testing practices.

Attaining dimensional accuracy in CNC-machined parts is essential for high-precision manufacturing, especially when working with materials that exhibit varying mechanical and thermal characteristics. This research provides a thorough experimental comparison of manual and automated metrological systems, specifically the Digital Vernier Caliper (DVC) and Coordinate Measuring Machine (CMM), as applied to five different engineering alloys through five progressively machined axial zones. The study assesses absolute error, relative error, standard deviation, and measurement repeatability, factoring in material hardness, thermal conductivity, and surface changes due to machining. The results indicate that DVC performance is significantly affected by operator input and surface irregularities, with standard deviations reaching 0.03333 mm for Bronze C51000 and relative errors surpassing 1.02% in the initial zones. Although DVC occasionally showed lower absolute errors (e.g., 0.206 mm for Aluminum 6061), these advantages were countered by greater uncertainty and poor repeatability. In comparison, CMM demonstrated enhanced precision and consistency across all materials, with standard deviations below 0.0035 mm and relative errors being neatly within the 0.005–0.015% range, even with challenging alloys like Stainless Steel 304. Furthermore, a Principal Component Analysis (PCA) was conducted to identify underlying measurement–property relationships. The PCA highlighted clear groupings based on sensitivity to error in manual versus automated methods, facilitating predictive classification of materials according to their metrological reliability. The introduction of multivariate modeling also establishes a new framework for intelligent metrology selection based on material characteristics and machining responses. These results advocate for using CMM in applications requiring precise tolerances in the aerospace, biomedical, and high-end tooling sectors, while suggesting that DVC can serve as an auxiliary tool for less critical evaluations. This study provides practical recommendations for aligning measurement techniques with Industry 4.0’s needs for accuracy, reliability, and data-driven quality assurance.

This study evaluates the accuracy and repeatability of craniofacial measurements with a 3D light scanner, specifically the EINSTAR scanner, in comparison to traditional caliper measurements for facial anthropometry. Eleven volunteers were assessed by two examiners, one experienced and one inexperienced, who performed direct caliper measurements and indirect measurements using the scanner. Results indicated minimal differences between caliper and scanner results, with overall high accuracy and reliability demonstrated by correlation coefficients. Despite the slightly longer scanning time, the benefits of 3D imaging, including detailed surface mapping and virtual modeling, justify its integration into clinical practice, particularly in maxillofacial surgery and craniofacial assessment. Craniofacial measurements obtained with the EINSTAR scanner showed excellent reliability and accuracy, which qualifies this method for clinical and scientific use.

Background/Objectives: Tumor growth delay is frequently used in preclinical experiments evaluating oncologic interventions. While treatment response in humans is based on imaging criteria for obvious reasons, manual caliper measurement of subcutaneous tumors is standard in animal studies. In a murine tumor model treated with immunotherapy (ImT) and radiotherapy (RT), the reliability of caliper measurements was tested by comparing normalized tumor growth delay (NTGD) rates derived from caliper- and image-based volumetrics. Methods: A 4T1 breast syngeneic murine model was used, in which thirty animals were inoculated in the right inguinal mammary fat pad and the right axilla. One RT fraction of 8 Gy was delivered to the right inguinal tumor on day 11 post-implant, and intraperitoneal ImT (PD-1 checkpoint inhibitor) injections were administered on days 11, 12, and 14. Each animal underwent three MRI scans (days 10, 17, and 20). Caliper measurements were also performed by two independent observers on the same days. The measurements were averaged and used to estimate ellipsoid tumor volumes. The acquired MRIs were used for image segmentation and volume estimation. Tumor volumes (days 17 and 20) were normalized against the baseline pre-treatment tumor volume (day 10). NTGD rates derived from hand- and image-based volumetrics were compared to assess the reliability of caliper vs. MRI estimation. Results: Caliper volumes between the two observers correlated at 0.799 (Pearson, p < 0.001). The averaged caliper volumes correlated with MRI volumes at 0.897 (Pearson, p < 0.001). Absolute volume differences between caliper and MRI increased with tumor growth. NTGD-derived rates showed no correlation, with only 15% of NTGD caliper rates falling within 10% of the MRI rates. Conclusions: NTGD rate based on caliper volumes is a suitable measure of treatment response in preclinical studies. In the experiment described herein, caliper-derived NTGD rates did not correlate with MRI ground truth. These findings suggest that more accurate tumor volumetrics, derived from stored and verifiable medical imaging sources, should be used in preclinical assessment of oncologic interventions instead of standard caliper estimates.

Accurate and efficient measurement of tree diameter at breast height (DBH) is essential for forest inventory and management. While traditional methods are time-consuming, new smartphone-based LiDAR applications like ForestScanner promise rapid, cost-effective solutions. However, their performance across diverse forest ecosystems requires thorough evaluation. This study aimed to assess the accuracy and time efficiency of the ForestScanner app for plot-level DBH measurements compared to manual caliper methods under varied growing conditions in Romania. One hundred circular plots (approx. 300 m² each) were established in forests near Brașov City, encompassing diverse forest tree species, ages, topographies, and understory conditions. DBH of 987 trees was measured manually with calipers and digitally using the ForestScanner app on a LiDAR-equipped iPhone. Time consumption for plot establishment, manual DBH, and app-based DBH measurements was recorded. Accuracy was assessed using bias, mean absolute error (MAE), and root mean squared error (RMSE), with heteroskedasticity checked via Breusch-Pagan and White tests. ForestScanner showed a negligible overall bias (-0.003 cm), but MAE reached 3.66 cm when all measurements were included. Occlusion by vegetation or nearby trees significantly impacted the app’s accuracy; for non-obstructed trees (n = 824), bias was +0.26 cm with an MAE of 2.07 cm. Manual DBH measurement averaged 14 seconds/tree, while ForestScanner averaged 16 seconds/tree. Plot establishment time and measurement time were influenced by tree density. ForestScanner offers a user-friendly, free tool for DBH measurement and tree mapping, but its accuracy may be affected by occlusion. On the other hand, the app comes equipped with several useful features, such as documenting the plots by LiDAR point clouds, real-time DBH measurement, and data storage, while returning comparable time efficiencies. Future work should focus on more diverse forest types to refine its practical application in forestry.

When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970s, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods—or developing methods related to matching—do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.

We propose new optimal matching techniques for large administrative data sets. In current practice, very large matched samples are constructed by subdividing the population and solving a series of smaller problems, for instance, matching men to men and separately matching women to women. Without simplification of some kind, the time required to optimally match 𝑇 treated individuals to 𝑇 controls selected from 𝐶 ≥ 𝑇 potential controls grows much faster than linearly with the number of people to be matched—the required time is of order 𝑂{(𝑇+𝐶)3}—so splitting one large problem into many small problems greatly accelerates the computations. This common practice has several disadvantages that we describe. In its place, we propose a single match, using everyone, that accelerates the computations in a different way. In particular, we use an iterative form of Glover's algorithm for a doubly convex bipartite graph to determine an optimal caliper for the propensity score, radically reducing the number of candidate matches; then we optimally match in a large but much sparser graph. In this graph, a modified form of near-fine balance can be used on a much larger scale, improving its effectiveness. We illustrate the method using data from US Medicaid, matching children receiving surgery at a children's hospital to similar children receiving surgery at a hospital that mostly treats adults. In the example, we form 38,841 matched pairs from 159,527 potential controls, controlling for 29 covariates plus 463 Principal Surgical Procedures, plus 973 Principal Diagnoses. The method is implemented in an R package bigmatch available from CRAN.

This research introduces a strategy to integrate blockchain technology with Internet of Things. Amalgamation of blockchain technology and Internet of Things is vital as one of them offers to connect patients remotely and other provides a higher level of privacy, secure decentralized system and immutable data storage. The studies in the past were merely storing patient vitals overlooking the significance of medical reports. A medical history remains incomplete without storing medical reports. This research is offering a technique to store both medical reports and patient vitals on the blockchain ledger. To prevent overwhelming the blockchain network, a node.js application is developed which store the medical reports on the IPFS server and retrieve their corresponding hash values. Thereafter, these hash values along with the patient details and vitals are then transmitted to blockchain ledger. This study makes use of MAX30100 and DS18B20 sensors to monitor heart rate, blood oxygen and body temperature. ESP32 microcontroller is used to integrate these sensors and fetch their data periodically. Hyperledger fabric blockchain framework is used for maintaining the ledger and Hyperledger caliper tool is used to evaluate the overall performance of the proposed system. The performance is computed on three key parameters: reliability, throughput and latency. Reliability is evaluated in two phases, one with caliper tool and another with real RPM (Remote Patient Monitoring) unit. In the first phase, caliper tool transmitted 1500 transactions which are then verified by reading the ledger. In the second phase, RPM unit transmitted 480 transactions to blockchain ledger within 8 h. This study confirms that all transmitted transactions are successfully recorded on the blockchain ledger without any loss or failure. Medical reports submitted on IPFS server are also cross verified and found to be intact. The second experiment is carried out using two, four and eight workers attempting to execute 1000 transactions cumulatively at 40, 80 and 160 TPS (Transactions Per Second) respectively. It is noteworthy that when caliper tool is configured to execute transactions at 40 and 80 TPS, the achieved TPS remain unchanged. In contrast to this, when caliper tool is configured to send transactions at 160 TPS it could only achieve the transaction rate of 94 TPS. The peak of average latency is recorded as 0.45 s when transactions are executed at 94 TPS. The lowest latency is observed as 0.24 s at 40 TPS. As far as the throughput is concerned, the highest throughput is observed as 91.4 TPS when the caliper tool is attempting to execute transactions at 94 TPS. The system could achieve throughput of 39.8 and 79.4 TPS when caliper attempts to send transactions at 40 and 80 TPS respectively. The unique contribution of this study is to converge Hyperledger fabric blockchain framework, InterPlanetary File System (IPFS) and Internet of Things health sensors to develop a comprehensive solution for storing and retrieving the medical histories of remote patients, effectively managing both patient vitals and complex medical reports without compromising reliability and overall throughput.