Search Results (1,676)

Vegetation indices (VIs) are a widely adopted and straightforward tool for non-contact estimation of chlorophyll and carotenoid content in plant leaves. However, VI-based method accuracy depends critically on instrument configuration and calibration procedures. This study aimed to evaluate the sensitivity of VI-based pigment assessment to variations in spectral channel parameters (central wavelength and bandwidth) as well as to changes in calibration details defined by the specific VI formula. Pigment content was measured in leaves of Lactuca sativa L. and Cucumis sativus L. at contrasting developmental stages using VI-based reflection spectroscopy across the 450–950 nm spectral range with various protocols and spectrophotometry as the reference method. VI values were calculated with varying central wavelength and widths of spectral bands, and across different VI formulas. Comparative analysis of the obtained measurements revealed that even minor shifts in central wavelengths of less than 20 nm or the use of an alternative index formula could lead to relative errors of 42–77% in the estimation of chlorophylls and carotenoids content, while changes in bandwidth had a much smaller impact, resulting in only 2–5% relative errors. Even with identical parameters of spectral channels, the choice of an appropriate VI and its regression model could introduce significant errors, ranging from 36% to 86%. These findings highlight the critical role of instrument specifications and calibration models in the VIs-based method accuracy and stability, as measurement errors can lead to suboptimal agronomic decisions. Moreover, our study underscores that comparing results from different sensors or platforms can be unreliable unless the channel parameters and calibration details are clearly specified. Therefore, standardization and transparency in VIs assignment is vital to ensure reproducibility and cross-compatibility in non-destructive pigment monitoring by using various devices. Full article

Titanium (Ti) alloy sheets are important mechanical and structural components. However, thickness deviations may occur during the production of Ti-alloy sheets, significantly compromising product quality and structural safety. Eddy current testing (ECT) is a common method for measuring the thickness deviation of metal sheets. Nevertheless, conventional ECT methods often rely on complex calibration procedures or iterative inversion algorithms, thereby limiting their applicability. It was found that when low-frequency ECT excitation is used, such that the eddy current penetration depth exceeds three times the maximum target thickness of the Ti-alloy sheet, the tangent of the ECT coil impedance phase exhibits a linear relationship with the thickness. Based on this observation, by analyzing the low-frequency ECT response of Ti-alloys and separating the real and imaginary parts of the impedance under approximate conditions, a phase feature model was developed. The model effectively describes the linear dependence of the phase tangent on the thickness of the Ti-alloy sheet, offering a succinct characterization. The measurement method based on this model thereby allows for direct thickness calculation from the measured coil impedance without requiring master-curve calibration or iterative computation. Experiments were conducted using a custom-designed ECT coil and impedance analyzer to measure different Ti-alloy specimens. The results indicate that the measurement error was less than 3.5%. This research provides a theoretical foundation as well as a straightforward engineering solution for online, high-speed thickness measurement of Ti-alloy sheets. Full article

Precise regulation of micropipette outlet flow is critical for fluorescence imaging in vivo micromanipulations. In such procedures, a micropipette with a micro-sized opening is driven by gas pressure to deliver internal solution into the in vivo environment. The outlet flow rate needs to be precisely regulated to ensure a uniform and stable fluorescence distribution. However, conventional manual pressure injection methods face inherent limitations, including insufficient precision and poor reproducibility. Existing commercial microinjection systems lack a quantitative relationship between pressure and flow rate. And existing calibration methods in the field of microfluidics suffer from a limited flow-rate measurement resolution, constraining the establishment of a precise pressure–flow quantitative relationship. To address these challenges, we developed a closed-loop pressure regulation system with 1 Pa-level control resolution and established a quantitative calibration of the pressure–flow relationship using a droplet-based method. The calibration revealed a linear relationship with a mean pressure–flow gain of 4.846 $\times {10}^{-17}{\mathrm{m}}^3·{\mathrm{s}}^{-1}·{\mathrm{P}\mathrm{a}}^{-1}$ (R² > 0.99). Validation results demonstrated that the system achieved the target outlet flow rate with a flow control error less than 10 fL/s. Finally, the application results in brain-slice environment confirmed its capability to maintain stable fluorescence imaging, with fluorescence intensity fluctuations around 1.3%. These results demonstrated that the proposed approach provides stable, precise, and reproducible flow regulation under physiologically relevant conditions, thereby offering a valuable tool for in vivo micromanipulation and detection. Full article

The Black Sea is a semi-enclosed basin subject to intense anthropogenic pressures and transboundary pollution, making reliable and comparable monitoring data essential for large-scale environmental assessments. However, national practices differ considerably, hindering data integration and coordinated reporting under international frameworks. This study, conducted within the Horizon 2020 project “Advancing Black Sea Research and Innovation to Co-develop Blue Growth within Resilient Ecosystems” (BRIDGE-BS), evaluated pollutant surveillance methodologies with a focus on heavy metals and priority organic contaminants (polycyclic aromatic hydrocarbons, polychlorinated biphenyls, organochlorine pesticides). Standard Operating Procedures (SOPs) were collected from institutions across Black Sea countries and systematically compared for water, sediment, and biota matrices. The analysis revealed shared reliance on internationally recognized techniques but also heterogeneity in sediment fraction selection, digestion and extraction conditions, instrumental approaches, and quality assurance/quality control (QA/QC) documentation. To complement this assessment, an intercalibration (IC) exercise was organized through the QUASIMEME proficiency testing scheme, accompanied by a follow-up structured questionnaire sent to participant institutions. While individual results remain confidential, collective feedback highlighted common challenges in calibration, blank correction, certified reference materials (CRMs) availability, digestion variability, instrument maintenance, and the reporting of uncertainty and detection limits. Together, these findings confirm that harmonization in the Black Sea requires not only improved comparability of laboratory methods but also the future alignment of assessment methodologies, including indicators and thresholds, to support coherent, basin-wide environmental evaluations under regional conventions and EU directives. Full article

Background/Objectives: The role of α-synuclein (α-syn) in the pathogenesis of Parkinson’s disease (PD) or neurodegenerative diseases such as Lewy body dementia (LBD) and multiple system atrophy (MSA) is commonly accepted. Through different physiological dysfunctions, abnormal forms of α-syn are generated. These abnormal aggregates accumulate and alter pre- and postsynaptic transmission, in particular that of dopamine. Thus, the development of a diagnostic biomarker of synucleinopathies remains crucial and challenging. The development of an α-syn positron emission tomography (PET) radiopharmaceutical may be suitable to early diagnose and stratify patients, follow up disease progression, and evaluate future therapies. Methods: To develop a selective α-syn PET tracer, we synthesized an original series based on alkynyl(aza)indoles. Fifteen final ligands were synthesized bearing indoles or azaindoles from one side of the alkyne and a substituted phenyl ring for the opposite side of the alkyne. The final ligands were tested to determine Ki and/or Kd toward α-syn, tau, and Aβ. Results: The SAR showed that the indole series exhibited moderate to low affinity for α-syn and, moreover, lower Ki toward Aβ and tau (i.e., compound 39, Ki_(αsyn) 21.7 nM, Ki_(Aβ) 64.4 nM, Ki_(Tau) 27.6 nM), highlighting the low potency of these series to afford an α-syn tracer. The introduction of a nitrogen on the different positions of the phenyl to obtain the corresponding azaindoles resulted for most of the compounds in better affinity for α-syn and selectivity towards Aβ compared to the indole analogs (i.e., compound 43, Ki_(αsyn) 4.7 nM, Ki_(Aβ) 24.4 nM, and Ki_(Tau) 4.61 nM). A fluorinated azaindole derivative was prepared with a view to obtaining a ¹⁸F tracer and exhibited the highest affinity for α-syn but without selectivity against tau and Aβ. The radiosynthesis of [¹⁸F]45 was performed in a two-step procedure starting from the tosylated and protected precursor. [¹⁸F]45 was obtained in 85 ± 5 min with a radiochemical yield of 32 ± 3%. Molar activity, determined from a calibration with stable 45, was around 130 GBq/µmole. The dynamic PET imaging showed that [¹⁸F]45 was able to cross the blood–brain barrier, but non-specific uptake was observed, confirming the in vitro results. Conclusions: Although promising nanomolar affinity for the target, the new tracer showed mainly non-specific in vivo uptake in the rat brain, indicating that further pharmacomodulations on the azaindole series are required. Full article

The Kennedy model provides a flexible and mathematically consistent framework for modeling the term structure of interest rates, leveraging Gaussian random fields to capture the dynamics of forward rates. Building upon our earlier work, where we developed both theoretical results—including novel proofs of the martingale property, connections between the Kennedy and HJM frameworks, and parameter estimation theory—and practical calibration methods, using maximum likelihood, Radon–Nikodym derivatives, and numerical optimization (stochastic gradient descent) on simulated and real par swap rate data, this study extends the analysis in several directions. We derive detailed formulas for the volatilities implied by the Kennedy model and investigate their asymptotic properties. A comprehensive sensitivity analysis is conducted to evaluate the impact of key parameters on derivative prices. We implement an industry-standard Monte Carlo method, tailored to the conditional distribution of the Kennedy field, to efficiently generate scenarios consistent with observed initial forward curves. Furthermore, we present closed-form pricing formulas for various interest rate derivatives, including zero-coupon bonds, caplets, floorlets, swaplets, and the par swap rate. A key advantage of these results is that the formulas are expressed explicitly in terms of the initial forward curve and the original parameters of the Kennedy model, which ensures both analytical tractability and consistency with market-observed data. These closed-form expressions can be directly utilized in calibration procedures, substantially accelerating multidimensional nonlinear optimization algorithms. Moreover, given an observed initial forward curve, the model provides significantly more accurate pricing formulas, enhancing both theoretical precision and practical applicability. Finally, we calibrate the Kennedy model to market-observed caplet prices. The findings provide valuable insights into the practical applicability and robustness of the Kennedy model in real-world financial markets. Full article

Background/Objectives: The purpose of the study is to determine and compare the accuracy and efficiency of two dynamic navigation systems (DNS)—Navident (ClaroNav, Canada) and X-Guide (Nobel Biocare, Switzerland)—vs. a free-hand (FH) approach in performing endodontic microsurgery (EMS) on human cadavers. Methods: a total of 119 roots of six cadavers were randomly divided into three groups (Navident/X-Guide/FH). The cadavers’ jaws were scanned pre-operatively with computed tomography. The DICOM data were uploaded and digitally managed with software interfaces for registration, calibration, and virtual planning of EMS. Osteotomy was performed under DNS control and using a dental operating microscope (FH control group). Post-operative scans were taken with same settings as preoperative. Accuracy was then determined by comparing pre- and post-scans of coronal and apical linear, angular deviation, angle, length, and depth of apical resection. Efficiency was determined by measuring the procedural time of osteotomy, apicectomy, retro-cavity preparation, the volume of substance, and cortical bone loss, as well as iatrogenic complications. Outcomes were also evaluated in relation to different operators’ skill levels. Descriptive statistics and inferential analyses were conducted using R software (4.2.1). Results: DNS demonstrated better efficiency in osteotomy and apicectomy, second only to FH in substance and cortical bone loss. Both DNS approaches had similar accuracy. Experts were faster and more accurate than non-experts in FH, apart from resection angle, length and depth, and retro-cavity preparation time, for which comparison was not statistically significant. The Navident and X-guide groups had similar trends in increasing efficiency and accuracy of EMS. All complications in the FH group were performed by non-experts. The X-guide group demonstrated fewer complications than the Navident group. Conclusions: Both DNS appear beneficial for EMS in terms of accuracy and efficacy in comparison with FH, also demonstrating the decreasing gap of skill expertise between experts and novice operators. Through convenient use X-guide diminishes the level of iatrogenic complications compared to Navident. Full article

Laser-accelerated electron beams, in the so-called Very High-Energy Electron (VHEE) energy range, are of great interest for biomedical applications. For instance, laser-driven VHEE beams are envisaged to offer suitable compact accelerators for the promising field of FLASH radiotherapy. Radiobiology experiments carried out using laser-driven beams require the real-time knowledge of the dose delivered to the sample. We have developed an online dose monitoring procedure, using an Integrating Current Transformer (ICT) coupled to a suitable collimator, that allows the estimation of the delivered dose on a shot-to-shot basis under suitable assumptions. The cross-calibration of the measured charge with standard offline dosimetry measurements carried out with RadioChromic Films (RCFs) is discussed, demonstrating excellent correlation between the two measurements. Full article

Nowadays, bifacial photovoltaic (PV) technology has emerged as a key solution to enhance the energy yield of large-scale PV plants, especially when integrated with sun-tracking systems. This study investigates the quantification of bifaciality productivity for two multi-MW PV plants in southern Italy (Sicily) equipped with monocrystalline silicon bifacial modules installed on single-axis east–west tracking systems and aligned in the north–south direction. An optimized energy model was developed at the stringbox level, employing a dedicated procedure including data filtering, clear-sky condition selection, and numerical estimation of bifaciality factors. The model was calibrated using on-field measurements acquired during the first operational months to minimize uncertainties related to degradation phenomena. The application of the model demonstrated that the rear-side contribution to the total energy output is non-negligible, resulting in additional energy gains of approximately 5.3% and 3% for the two plants, respectively. Full article

Mechanical properties are critical for characterizing and fabricating advanced materials. While current characterization methods are well-established for the nanoscale and larger millimeter-scale, a significant gap exists in automated testing at the microscale. To address this, we propose an automated, rapid characterization method based on a microrobotic system. We first develop a 6-degree-of-freedom (DOF) microrobotic system for sample alignment and testing. An image processing method is then designed for real-time sample recognition, supplying essential feedback for both alignment and testing procedures. Furthermore, a soft force sensor is fabricated and calibrated to ensure precise force measurement. Experiments on copper wires and graphite films demonstrate the method’s high precision and reliability. This work provides a robust solution for microscale mechanical property characterization, offering significant potential for advanced material development. Full article

The manufacturing industry, in general, and the plastic industry, in particular, have been developing new materials and process methods that need a correct study and optimization. Nowadays, the main approach to optimize these processes is using numerical methods and, in the case of particulate materials, the Discrete Elements Method to estimate the particles interactions. But those mathematical models use some parameters that depend on the material and must be calibrated, thus requiring an important computational and experimental cost. In this study, we integrate different speed-up procedures and present a general calibration method of Low-Density Polyethylene particles, to obtain the calibrated solid density and Poisson’s ratio of the material, the restitution, static and rolling friction factors in the particle-to-particle and particle-to-wall interactions, and the contact model variables (damping factor, stiffness factor, and energy density). For this calibration, four different tests were carried out, both experimentally and with simulations, obtaining the bulk density, the repose and shear angles, and the dropped powder. All these response variables were compared between simulations and experimental tests, and using genetic algorithms, the input parameters (design variables) were calibrated after 85 iterations, obtaining a Mean Absolute Percentage Error of the response variables lower than 2% compared to the experimental results. Full article

High-sensitivity accelerometers are essential for spacecraft micro-vibration monitoring. This study proposes a procedure to facilitate precise on-ground calibration of such accelerometers with a limited operational range by rotating to multiple positions with its input axis mounted along the horizontal tilt axis of a two-axis indexing device. Each single-axis accelerometer unit of a self-developed tri-axial nano-g accelerometer was respectively tested with its various reference axes along the rotation axis for identifying the parameters of their model equations including higher-order terms. The minute tilt axis deviation of the test equipment from the horizontal plane and the accelerometer’s higher-order response to gravity during calibration are corrected for application in the microgravity environment. Errors of accelerometer biases and scale factors are satisfactorily improved, respectively, to ±2% and ±0.01 mg, by at least one order of magnitude. Parameters of all three units of the accelerometer are unified into one coordinate frame defined by the accelerometer mounting surface. Acceleration measured by our accelerometer shows consistency with the other collocated one in a space mission. Full article

Background: Perioperative hypotension is a frequent but underrecognized complication during transcatheter aortic valve implantation (TAVI). Although reduced left ventricular ejection fraction (EF) and low baseline blood pressure have been linked to hemodynamic instability, the role of metabolic comorbidities and procedural factors remains less well established. Methods: We retrospectively analyzed 123 patients who underwent transfemoral TAVI between June 2016 and June 2022. Perioperative hypotension was defined as a sustained systolic blood pressure < 90 mmHg or ≥30% reduction from baseline for at least 5 min. Clinical, laboratory, and procedural predictors were assessed using multivariate logistic regression, and model performance was evaluated by ROC curve analysis. Results: Perioperative hypotension occurred in 57% of patients. Independent predictors were diabetes mellitus (OR 2.79, 95% CI 1.03–7.56, p = 0.044), reduced EF (<50%) (OR 2.87, 95% CI 1.13–7.31, p = 0.027), lower baseline diastolic blood pressure (OR 0.935 per mmHg, 95% CI 0.893–0.978, p = 0.004), and longer procedural duration (OR 1.038 per minute, 95% CI 1.001–1.076, p = 0.044). The predictive model demonstrated good calibration and discrimination (AUC 0.844). Patients with hypotension had significantly higher in-hospital mortality (12.9% vs. 1.9%, p = 0.027) and longer ICU stay. An exploratory finding suggested less frequent use of sugammadex among hypotensive patients (11.4% vs. 32.1%, p = 0.005). Conclusions: Perioperative hypotension is common during TAVI and strongly associated with early mortality. Our study uniquely identifies diabetes mellitus as an independent predictor, alongside ventricular dysfunction, baseline blood pressure, and procedural duration. These findings suggest that careful preprocedural risk stratification, hemodynamic vigilance, and optimization of anesthetic management may improve outcomes in vulnerable patients. Full article

Hospital fraud detection has often relied on periodic audits that miss evolving, internet-mediated patterns in electronic claims. An artificial intelligence and machine learning pipeline is being developed that is leakage-safe, imbalance aware, and aligned with operational capacity for large healthcare datasets. The preprocessing stack integrates four tables, engineers 13 features, applies imputation, categorical encoding, Power transformation, Boruta selection, and denoising autoencoder representations, with class balancing via SMOTE-ENN evaluated inside cross-validation folds. Eight algorithms are compared under a fraud-oriented composite productivity index that weighs recall, precision, MCC, F1, ROC-AUC, and G-Mean, with per-fold threshold calibration and explicit reporting of Type I and Type II errors. Multilayer perceptron attains the highest composite index, while CatBoost offers the strongest control of false positives with high accuracy. SMOTE-ENN provides limited gains once representations regularize class geometry. The calibrated scores support prepayment triage, postpayment audit, and provider-level profiling, linking alert volume to expected recovery and protecting investigator workload. Situated in the Future Internet context, this work targets internet-mediated claim flows and web-accessible provider registries. Governance procedures for drift monitoring, fairness assessment, and change control complete an internet-ready deployment path. The results indicate that disciplined preprocessing and evaluation, more than classifier choice alone, translate AI improvements into measurable economic value and sustainable fraud prevention in digital health ecosystems. Full article

The quantification of volatile compounds in virgin olive oil poses several analytical challenges due to the existence of different concentrations, chemical families, and the possible matrix effect. Accurate quantification, using adequate methodological calibration and statistical procedures, is essential for obtaining reliable results. The aim of this work was to develop and validate an analytical–statistical approach for the quantification of volatile compounds in virgin olive oil. Therefore, several analytical parameters were determined for four calibrations. The ordinary least square (OLS) linear adjustment was selected over the weighted least square due to the homoscedasticity of the variable errors. Additionally, standard addition (AC) and AC with an internal standard (IS) exhibited greater variability, whereas external matrix-matched calibration (EC) was identified as the most reliable approach for quantifying volatile compounds in virgin olive oil. The employment of an IS did not improve the performance of the method in any case. Thus, based on the statistical results, the OLS linear adjustment with EC was selected as the best statistical–analytical approach for quantifying volatiles in olive oil matrices. The volatiles of nine virgin olive oil samples were quantified, applying different methodological calibrations, and no differences were detected, underscoring EC as a superior alternative. Full article