Search Results (83)

Background: Gingival recession is a common condition involving apical displacement of the gingival margin, leading to root surface exposure and associated complications such as dentin hypersensitivity and root caries. Among the most effective treatment options are the tunneling technique (TUN) and the coronally advanced flap (CAF), both combined with connective tissue grafts (CTGs). This study aimed to evaluate and compare the clinical outcomes of TUN + CTG and CAF + CTG in terms of root coverage and keratinized tissue width (KTW) over a 6–12-month follow-up. Methods: A randomized, double-blind clinical trial was conducted following CONSORT guidelines (ClinicalTrials.gov ID: NCT06228534). Participants were randomly assigned to receive either TUN + CTG or CAF + CTG. Clinical parameters, including gingival recession depth (REC) and KTW, were assessed at baseline as well as 6 months and 12 months postoperatively using a calibrated periodontal probe. Statistical analysis was performed using descriptive statistics and linear mixed models to compare outcomes over time, with a significance level set at 5%. Results: Both techniques demonstrated significant clinical improvements. At 6 months, mean root coverage was 100% in CAF + CTG cases and 97% in TUN + CTG cases, while complete root coverage (REC = 0) was observed in 100% and 89% of cases, respectively. At 12 months, root coverage remained stable, at 99% in the CAF + CTG group and 97% in the TUN + CTG group. KTW increased in both groups, with higher values observed in the CAF + CTG group (3.53 mm vs. 3.11 mm in TUN + CTG at 12 months). No significant postoperative complications were reported. Conclusions: Both TUN + CTG and CAF + CTG are safe and effective techniques for treating RT1 and RT2 gingival recession, offering high percentages of root coverage and increased KTW. While CAF + CTG achieved slightly superior coverage and tissue gain, the TUN was associated with better aesthetic outcomes and faster recovery, making it a valuable alternative in clinical practice. Full article

Heterozygous mutations in the GBA1 gene, encoding the enzyme glucocerebrosidase (GCase), are major risk factors for Parkinson’s Disease (PD). Ambroxol, a small chaperone originally used as a mucolytic agent, has been shown to cross the blood–brain barrier, enhance GCase activity, and reduce α-synuclein levels, making it a promising therapeutic candidate for disease-modifying effects in GBA1-associated PD (GBA1-PD). This study aimed to develop a method to quantify ambroxol levels in human plasma and cerebrospinal fluid (CSF) using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Ambroxol was determined by online solid-phase extraction (SPE), coupled with LC-MS/MS, by gradient elution on a monolithic column. Detection employed a 3200 QTRAP tandem mass spectrometer in the positive electrospray ionization mode. Calibration curves exhibited linearity across the analyzed ranges in both plasma and CSF. The recovery rate ranged from 106.7% to 113.5% in plasma and from 99.0% to 103.0% in CSF. No significant matrix effect was observed. Intra-day and inter-day precisions were below 11.8% in both matrices, and accuracy ranged from 89.9% to 103.1% in plasma and from 96.3% to 107.8% in CSF. We evaluated and confirmed the stability of the analyte in plasma and CSF across various storage conditions. The method was successfully validated according to European Medicine Agency (EMA) guidelines and its applicability was confirmed in the context of a multicenter, randomized, double-blind, placebo-controlled, phase II study, designed to monitor the ambroxol levels in the plasma and CSF of GBA1-PD. Full article

Proper determination of the bottomhole pressure in a gas lift well is essential to enhance production, tackle operating concerns, and use the least amount of gas. Mechanistic models, empirical correlation, and hybrid models are usually limited by the requirements for calibration, large amounts of inputs, or limited scope of work. Through this study, sixteen well-tested machine learning (ML) models, such as genetic programming-based symbolic regression and neural networks, are developed and studied to accurately predict flowing BHP at the perforation depth, using a dataset from 304 gas lift wells. The dataset covers a variety of parameters related to reservoirs, completions, and operations. After careful preprocessing and analysis of features, the models were prepared and tested with cross-validation, random sampling, and blind testing. Among all approaches, using the L-BFGS optimizer on the neural network gave the best predictions, with an R² of 0.97, low errors, and better accuracy than other ML methods. Upon using SHAP analysis, it was found that the injection point depth, tubing depth, and fluid flow rate are the main determining factors. Further using the model on 30 unseen additional wells confirmed its reliability and real-world utility. This study reveals that ML prediction for BHP is an effective alternative for traditional models and pressure gauges, as it is simpler, quicker, more accurate, and more economical. Full article

New methods for real-time materials phase identification based upon focal construct tomography (FCT) have been examined. Such quantitative assessment has significant potential in sectors where in-line analysis is required, including screening within aviation security. As a recent component of work programs developing FCT, its capability for accurate, quantitative analysis has been assessed for the first time. Diffraction signatures from mixed-phase materials were acquired from an energy-dispersive FCT system running under normal operational conditions. A calibration curve was constructed from the spectra and subsequently employed to assess the composition of ‘blind’ samples. The results demonstrated that this approach was able to precisely predict the polymorphic phase composition of samples to ±5 wt%. Conclusions: The potential impact of these findings is significant and will enable applications of FCT beyond those requiring a phase identification to those necessitating quantification, such as counterfeit medicines, pharmaceutical quality assurance, aging of explosives, and cement production. Full article

The theoretical part of this paper is devoted to a class of distributed blind calibration algorithms for large sensor networks based on consensus. The basic blind calibration method starts from affine sensor models and calibration functions, aiming to equalize corrected sensor offsets and gains without requiring any a priori knowledge of the measured signal. The main focus is to systematically and rigorously analyze the behavior of the calibration algorithms of the stochastic approximation type under nonlinear sensor models and stochastic environments, and to provide recommendations that are relevant to practice. It is demonstrated that the calibration algorithm—based on consensus with respect to all the calibration parameters—is far less robust to unknown sensor nonlinearities than the modified algorithm, taking one micro-calibrated sensor as a reference. Stability proofs of the algorithms are given in the bounded input–bounded output sense. The influences of measurement and communication noises are also analyzed using the theory of stochastic approximation. Numerous simulation results provide a comprehensive picture of the algorithm properties that are relevant to practice. This is followed by an important verification of the theoretical results, obtained by applying the analyzed blind calibration algorithms to an originally designed multichannel instrument for aerodynamic pressure sensing. A description of the new instrument is given, together with essential aspects of the implementation of the blind calibration algorithm. It is shown that the selected algorithm can be seen as a simple and efficient practical tool for blind online real-time re-calibration of complex sensor networks during normal system operations. Full article

Cocoa is a major commodity in the global food industry. Heavy metal contamination, particularly cadmium (Cd), raises significant concerns. This work demonstrates the use of laser-induced breakdown spectroscopy (LIBS) for fast Cd quantification in commercial cocoa powder across a wide range of concentrations (70–5000 ppm). Cocoa powder presents unique challenges due to its physical properties, such as the tendency to soften and liquefy at elevated temperatures, which complicates sample preparation. To address these issues, a mechanical mixing and pelletization protocol was implemented to ensure uniformity. Pellets were doped with known cadmium concentrations for calibration. Cadmium atomic lines at 340.36 and 361.05 nm were used to construct quantification curves. A special algorithm for background subtraction was implemented, and the LIBS plasma was characterized to ensure local thermodynamic equilibrium conditions. Out of eighteen samples, five double-blinded unknowns were evaluated. The concentrations agreed well within normalized standard deviations of 9.73% and 5.88% for the two cadmium lines. The limits of detection for the lines were 0.4 and 0.08 μg/g, respectively. LIBS is confirmed as a rapid and versatile analytical tool for Cd detection and quantification in complex food matrices, with potential applications in field-based and industrial monitoring systems. Full article

As the number of devices using wireless communications increases, the amount of usable radio frequency spectrum becomes increasingly congested. As a result, the need for robust, adaptive communications to improve spectral efficiency and ensure reliable communication in the presence of interference is apparent. One solution is using beamforming techniques on digital phased-array receivers to maximize the energy in a desired direction and steer nulls to remove interference; however, traditional phased-array beamforming techniques used for interference removal rely on perfect calibration between antenna elements and precise knowledge of the array configuration. Consequently, if the exact array configuration is not known (unknown or imperfect assumption of element locations, unknown mutual coupling between elements, etc.), these traditional beamforming techniques are not viable, so a beamforming approach with relaxed requirements (blind beamforming) is required. This paper proposes a novel blind beamforming approach to address complex narrowband interference in spectrally congested environments where the precise array configuration is unknown. The resulting process is shown to suppress numerous interference sources, all without any knowledge of the primary signal of interest. The results are validated through wireless laboratory experimentation conducted with a two-element array, verifying that the proposed beamforming approach achieves a similar performance to the theoretical performance bound of receiving packets in additive white Gaussian noise (AWGN) with no interference present. Full article

In traffic surveillance systems, accurate camera–LiDAR calibration is critical for effective detection and robust environmental recognition. Due to the significant distances at which sensors are positioned to cover extensive areas and minimize blind spots, the calibration search space expands, increasing the complexity of the optimization process. This study proposes a novel target-less calibration method that leverages dynamic objects, specifically, moving vehicles, to constrain the calibration search range and enhance accuracy. To address the challenges of the expanded search space, we employ a genetic algorithm-based optimization technique, which reduces the risk of converging to local optima. Experimental results on both the TUM public dataset and our proprietary dataset indicate that the proposed method achieves high calibration accuracy, which is particularly suitable for traffic surveillance applications requiring wide-area calibration. This approach holds promise for enhancing sensor fusion accuracy in complex surveillance environments. Full article

The profile of Volatile Organic Compounds (VOCs) may help prioritise at-risk groups for early cancer detection. Urine sampling has been shown to provide good disease accuracy whilst being patient acceptable compared to faecal analysis. Thus, in this study, urine samples were examined using an electronic nose with metal oxide gas sensors and a solid-phase microextraction sampling system. A calibration dataset (derived from a previous study) with CRC-positive patients and healthy controls was used to train a radial basis function neural network. However, a blinded analysis failed to detect CRC accurately, necessitating an enhanced data-processing strategy. This new approach categorised samples by significant bowel diseases, including CRC and high-risk polyps. Retraining the neural network showed an area under the ROC curve of 0.88 for distinguishing CRC versus non-significant bowel disease (without CRC, polyps or inflammation). These findings suggest that, with appropriate training sets, urine VOC analysis could be a rapid, low-cost method for early detection of precancerous colorectal polyps and CRC. Full article

Background: Both listening to music during warm-up and consuming caffeine before exercise have been independently shown to enhance athletic performance. However, the potential synergistic effects of combining these strategies remain largely unexplored. To date, only two studies have reported additional benefits to combining music during warm-up with a caffeine dose of 3 mg/kg on taekwondo-specific performance tasks. However, these studies did not evaluate whether this combination produces additive or synergistic effects on other types of sports performance. The present study aimed to assess the effects of listening to music alone or combined with caffeine intake on performance in the Wingate anaerobic test (WAnT) in physically active subjects. Methods: Twenty-four physically active male participants took part in this randomized, double-blind, crossover experiment. Participants underwent WAnT performance evaluations under three conditions: (i) no intervention (control; CON); (ii) music plus placebo (Mus + PLA), involving the intake of a placebo (maltodextrin) 60 min prior and self-selected high-tempo music during warm-up; and (iii) music plus caffeine (Mus + CAF), involving the intake of 3 mg/kg of caffeine 60 min prior and self-selected high-tempo music during warm-up. Under all conditions, participants wore the same Bluetooth headphones (with or without music), performed a 10 min standardized warm-up, and completed the 30 s WAnT with a load of 7.5% of their body weight on a calibrated ergometer. Power output was recorded at a frequency of 1 Hz throughout the exercise. The Feeling Scale was assessed both before and after the exercise test, while heart rate (HR) and the rating of perceived exertion (RPE) were measured immediately following the exercise. Results: Mus + PLA and Mus + CAF significantly improved peak power, mean power, and total work compared with CON (p < 0.05). Furthermore, peak power was higher in Mus + CAF than in Mus + PLA (p = 0.01). Post-exercise HR and RPE showed no significant differences across conditions (p > 0.05). Regarding the Feeling Scale (FS) before exercise, the Mus + PLA and Mus + CAF conditions showed significantly higher scores than CON (p < 0.05), while no differences were found after exercise. The perceived fitness metrics displayed no significant differences among conditions (p > 0.05), except for self-perceived power, which was higher in Mus + CAF than in CON (p = 0.03). Conclusions: Self-selected music during warm-up, either alone or combined with caffeine, significantly enhanced several WAnT performance metrics, including peak power, mean power, and total work. Remarkably, combining music with caffeine further improved peak power and increased self-perceived power compared with music alone. While listening to self-selected music during warm-up provided measurable benefits on anaerobic exercise performance, the combination of music and caffeine demonstrated additive effects, making it the optimal strategy for maximizing anaerobic performance. Full article

This study explores the tensile performance of blind rivet joints in galvanized steel sheets, focusing on their behavior under shear and normal load conditions. Blind rivets are frequently used in structural applications due to their ease of installation and ability to be applied from one side, making them highly effective in industries like aerospace and automotive. Two types of DIN 7337—4.8 × 8 blind rivets—galvanized steel St/St and stainless steel A2/A2—paired with galvanized steel sheets DX51D + Z275, were experimentally tested to assess how their material properties affect their joint strength, deformation patterns, and failure modes. Single-lap shear, double-lap shear, and pure normal load tests were conducted in multiple configurations to evaluate joint performance under varying loading conditions, simulating real-world stresses. Using custom-built equipment, controlled forces were applied perpendicular to the rivet joints to replicate practical loading conditions. The results revealed distinct differences in the load-bearing capacities of the two materials, offering valuable insights for applications where corrosion resistance and structural integrity are critical. Finite element analysis (FEA) was then used to simulate the behavior of the joints, with the results validated against experimental data. To enhance the reliability of numerical simulations in optimizing the design of rivet joints, a methodology was proposed to calibrate non-linear FEA models to experimental results, and a substantial agreement of 92.53% was achieved via optimization in ANSYS OptiSLang. This research contributes to our broader understanding of riveted connections, providing practical recommendations for assessing the performance of such joints in various engineering fields. Full article

This paper focuses on a method of rearview camera-based blind-spot detection and a lane change assistance system for autonomous vehicles, utilizing a convolutional neural network and lane detection. In this study, we propose a method for providing real-time warnings to autonomous vehicles and drivers regarding collision risks during lane-changing maneuvers. We propose a method for lane detection to delineate the area for blind-spot detection and for measuring time to collision—both utilized to ascertain the vehicle’s location and compensate for vertical vibrations caused by vehicle movement. The lane detection method uses edge detection on an input image to extract lane markings by employing edge pairs consisting of positive and negative edges. Lanes were extracted through third-polynomial fitting of the extracted lane markings, with each lane marking being tracked using the results from the previous frame detections. Using the vanishing point where the two lanes converge, the camera calibration information is updated to compensate for the vertical vibrations caused by vehicle movement. Additionally, the proposed method utilized YOLOv9 for object detection, leveraging lane information to define the region of interest (ROI) and detect small-sized objects. The object detection achieved a precision of 90.2% and a recall of 82.8%. The detected object information was subsequently used to calculate the collision risk. A collision risk assessment was performed for various objects using a three-level collision warning system that adapts to the relative speed of obstacles. The proposed method demonstrated a performance of 11.64 fps with an execution time of 85.87 ms. It provides real-time warnings to both drivers and autonomous vehicles regarding potential collisions with detected objects. Full article

Building optimisation techniques provide a rigorous framework for exploring new optimal design solutions. In this study, a genetic algorithm (GA) was used to investigate the energy efficiency of a vernacular architectural element (Rawshan) in Saudi Arabia. Two objectives were optimised using a GA simulation enhanced: energy consumption optimisation and useful daylight illuminance (UDI) optimisation. A calibrated simulation model of a typical house in Saudi Arabia was used in the study. Several metrics, such as light interference from shadows or other windows, were considered to indicate the importance of the Rawshan. Computational studies were performed using different climatic conditions, and the results were compared with and without a Rawshan element using the weather data of Mecca, Jeddah, Riyadh, and Al-Baha. In this study, the blind thicknesses on the front and sides of the Rawshan were used as optimisation variables. The results showed that using a GA with energy consumption as an objective can reduce energy consumption. One of the methods proposed in the paper can reduce energy consumption by 3.6%, 3.6%, and 16.6% for Mecca, Riyadh, and Al-Baha, respectively. The single-objective optimisation method demonstrated that Rawshan provided sufficient UDI in four cities: Mecca, Jeddah, Riyadh, and Al-Baha. The research provided optimised values for Rawshan blind thicknesses on the front and lateral sides under different optimisation constraints. The results showed that using Rawshans in modern building architecture can reduce energy consumption and improve useful daylight illuminance. Full article

Background: The World Health Organisation (WHO) included silver diamine fluoride (SDF) in the WHO Model List of Essential Medicines for the management of early childhood caries. SDF is typically available as a 38% aqueous solution, which is watery to apply. A 38% SDF gel has recently been developed, but its caries-arrest effectiveness remains unsubstantiated. The objective of this study is to determine whether the efficacy of a 38% SDF gel is non-inferior to a 38% SDF solution in arresting early childhood caries. Methods: This is a 30-month, randomised, active-controlled, parallel-group non-inferiority pragmatic clinical trial with two arms. The hypothesis is that the 38% SDF gel is not worse than the 38% SDF solution by a non-inferiority margin of 10% caries-arrest rate when applied semi-annually to preschool children. This trial will recruit 630 3-year-old kindergarten children through block randomisation to receive either an application of SDF gel or SDF solution on cavitated carious lesions in their primary teeth every 6 months. The primary outcome is the proportion of soft (active) carious tooth surfaces that turn hard (arrested) at the 30-month follow-up. The same calibrated dentist will conduct 6-monthly dental examinations in the kindergartens to assess the status of carious lesions over 30 months. The examiner, the children, and parents will be blinded to treatments. The parents will be surveyed on their child’s oral health-related behaviours and socioeconomic background to allow adjustment for effect modification. Results: If the anticipated results are obtained, clinicians can use the 38% SDF gel as an alternative of the 38% SDF solution in arresting early childhood caries. Conclusions: As SDF gel is cost-effective, non-invasive, and non-aerosol-generating, it can be widely recommended for caries control. Trial registration: ClinicalTrials.gov NCT06241261. Registered on 7 February 2024. Full article

This article reports a 110.2 MHz ultra-low-power phase-locked loop (PLL) for MEMS timing/frequency reference oscillator applications. It utilizes a 6.89 MHz MEMS-based oscillator as an input reference. An ultra-low-power, high-resolution phase-frequency detector (PFD) is utilized to achieve low-noise performance. Eliminating the reset feedback path used in conventional PFDs leads to dead/blind zone-free phase characteristics, which are crucial for low-noise applications within a wide operating frequency range. The PFD operates up to 2.5 GHz and achieves a linear resolution of 100 ps input time difference ($\Delta t_{in}$), without the need for any additional calibration circuits. The linearity of the proposed PFD is tested over a phase difference corresponding to aa $\Delta t_{in}$ ranging from 100 ps to 50 ns. At a 1 V supply voltage, it shows an error of <±1.6% with a resolution of 100 ps and a frequency-normalized power consumption ($P_n$) of 0.106 pW/Hz. The PLL is designed and fabricated using a TSMC 65 nm CMOS process instrument and interfaced with the MEMS-based oscillator. The system reports phase noises of −106.21 dBc/Hz and −135.36 dBc/Hz at 1 kHz and 1 MHz offsets, respectively. It consumes 6.709 $\mathsf{\mu }$W at a 1 V supply and occupies an active CMOS area of 0.1095 mm². Full article