Search Results (30,547)

This paper analyses a special case in evaluating the passive autoranging (AR) technique that dynamically extends the measurement range of a fiber Bragg grating/piezoelectric transducer (FBG/PZT) operating with a current transformer (CT) to realize a dual-purpose metering and protection photonic current transducer (PCT). The technique relies on shorting serially connected burden resistors operating with the CT, using MOSFET switches that react to a changing input current to extend measurement range. The rapid changes in the voltage at the FBG/PZT transducer that are associated with the MOSFET switching are then used on the FBG interrogator side to select the correct measurement range. However, when the MOSFET switching in the AR circuit occurs near the zero-crossing of the input current, the rapid changes in the voltage presented to the FBG/PZT no longer occur, rendering the correct range setting at the interrogator side problematic. The basic switching detection algorithm based on voltage derivative (dV/dt) thresholds proposed in the previous research is not sufficiently sensitive in these conditions, leading to incorrect range selection. To address this, a new detection algorithm based on temporal slope differencing around the zero-crossing is proposed as an additional detection mechanism for these special cases. Thus, the improved hybrid algorithm additionally computes the derivative dV/dt at the FBG/PZT voltage signal within a focused 6 ms temporal window centered around the zero-crossing point, a 3 ms window before and after each zero-crossing instance. It then compares the difference between these two values to a predefined threshold. If the difference exceeds the threshold, a switching event is identified. This method reliably detects even subtle switching events near zero crossings, enabling the accurate reconstruction of the burden current. The performance of the improved algorithm is validated through simulations and experimental results involving zero-crossing switching scenarios. Results indicate that the proposed algorithm improves MOSFET switching detection and facilitates reliable waveform reconstruction without requiring additional hardware. Full article

Artificial neural networks (ANNs) offer considerable advantages in predicting evaporation (EVAP), particularly in handling nonlinear relationships and complex interactions among factors like soil surface temperature (SST) and wind speed (WS). In Al Medina, Saudi Arabia, the connections among WS, SST at 5 cm, SST at 10 cm, and EVAP have been modeled using an ANN. This study demonstrates the practical effectiveness and applicability of the approach in simulating complex nonlinear dynamics in real-life systems. The modeling process employs time series data for WS, SST at both 5 cm and 10 cm, and EVAP, gathered from January to December (2002–2010). Four ANNs labeled T1–T4 were developed and trained with the feedforward backpropagation (FFBP) algorithm using MATLAB routines, each featuring a distinct configuration. The networks were further refined through the enumeration technique, ultimately selecting the most efficient network for forecasting EVAP values. The results from the ANN model are compared with the actual measured EVAP values. The mean square error (MSE) values for the optimal network topology are 0.00343, 0.00394, 0.00309, and 0.00306 for T1, T2, T3, and T4, respectively. Full article

This study reports the synthesis and comprehensive spectroscopic characterization of 4-indolylcyanamide (4ICA), a novel indole-derived infrared (IR) probe designed for assessing local microenvironments in biological systems. 4ICA was synthesized via a two-step procedure with an overall yield of 43%, and its structure was confirmed using high-resolution mass spectrometry and ¹HNMR. Fourier Transform Infrared (FTIR) spectroscopy revealed that the cyanamide group stretching vibration of 4ICA exhibits exceptional solvent-dependent frequency shifts, significantly greater than those of conventional cyanoindole probes. A strong linear correlation was observed between the vibrational frequency and the combined Kamlet–Taft parameter, underscoring the dominant role of solvent polarizability and hydrogen bond acceptance in modulating its spectroscopic behavior. Quantum chemical calculations employing density functional theory (DFT) with a conductor-like polarizable continuum model (CPCM) provided further insight into the solvatochromic shifts and suppression of Fermi resonance in high-polarity solvents such as DMSO. Additionally, IR pump–probe measurements revealed short vibrational lifetimes (~1.35 ps in DMSO and ~1.13 ps in ethanol), indicative of efficient energy relaxation. With a transition dipole moment nearly twice that of traditional nitrile-based probes, 4ICA demonstrates enhanced sensitivity and signal intensity, establishing its potential as a powerful tool for site-specific environmental mapping in proteins and complex biological assemblies using nonlinear IR techniques. Full article

The four buckets phase demodulation method is a widely used sinusoidal modulation and demodulation technique in interferometry. Strict calibration is essential to minimize nonlinear errors in subsequent measurements. The core of the algorithm calibration lies in determining the initial phase value of the modulation signal that matches the modulation depth while overcoming the influence of system phase delay. Currently, there are few systematic calibration methods specifically designed for optical fiber interferometry. This paper proposes a self-calibration method based on triangular wave mixing for four buckets phase demodulation in fiber optic interferometric probes, which efficiently achieves self-calibration of the phase demodulation while the measured object remains stationary. Simulations and experimental validations were conducted, demonstrating that the optimal initial phase value of 0.62 rad during phase demodulation can be accurately identified under static conditions. The calibrated phase value was then applied to the displacement measurement, where the target displacement was effectively detected, resulting in a root mean square (RMS) error of 3.0337 nm and an average error of 2.4479 nm. Full article

This paper presents an optimal control for autonomous vehicles navigating in intersection scenarios. The proposed controller is based on solving a Quadratic Programming optimization technique to provide a feasible control signal respecting actuator constraints. The proposed controller was implemented in a scale-sized vehicle and is executed using only on-board perception and computing systems to retrieve the state dynamics, i.e., an inertial measurement unit and a monocular camera, to compute the estimated states through intelligent computer vision algorithms. The stability of the error signals of the closed-loop system was proved both mathematically and experimentally, using standard performance indices for ten trials. The proposed technique was compared against LQR and MPC strategies, showing 67% greater accuracy than the LQR approach and 53.9% greater accuracy than the MPC technique, while turning during the intersection. Moreover, the proposed QP controller showed significantly greater efficiency by reducing the control effort by 63.3% compared to the LQR, and by a substantial 78.4% compared to the MPC. These successful results proved that the proposed controller is an effective alternative for autonomously navigating within intersection scenarios. Full article

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring. Full article

The soybean Glycine max (L.) Merrill is a key crop in Brazil’s agricultural sector and is essential for both domestic food security and international trade. However, water stress severely impacts its productivity. In this study, we examined the physiological and biochemical responses of soybean plants to various water regimes via hyperspectral reflectance (350–2500 nm) and machine learning (ML) models. The plants were subjected to eleven distinct water regimes, ranging from 100% to 0% field capacity, over 14 days. Seventeen key physiological parameters, including chlorophyll, carotenoids, flavonoids, proline, stress markers and water content, and hyperspectral data were measured to capture changes induced by water deficit. Principal component analysis (PCA) revealed significant spectral differences between the water treatments, with the first two principal components explaining 88% of the variance. Hyperspectral indices and reflectance patterns in the visible (VIS), near-infrared (NIR), and shortwave-infrared (SWIR) regions are linked to specific stress markers, such as pigment degradation and osmotic adjustment. Machine learning classifiers, including random forest and gradient boosting, achieved over 95% accuracy in predicting drought-induced stress. Notably, a minimal set of 12 spectral bands (including red-edge and SWIR features) was used to predict both stress levels and biochemical changes with comparable accuracy to traditional laboratory assays. These findings demonstrate that spectroscopy by hyperspectral sensors, when combined with ML techniques, provides a nondestructive, field-deployable solution for early drought detection and precision irrigation in soybean cultivation. Full article

Background: Hyaluronic acid (HA) fillers are commonly used for midface augmentation because of their biocompatibility and reversibility. Nonetheless, discussions continue about their effectiveness and safety relative to other options. This systematic review and meta-analysis assess the effectiveness, duration, and side effects of HA fillers in midface volume restoration. Methods: Following PRISMA guidelines, a thorough search was performed on PubMed, CENTRAL, Web of Science, Scopus, and EMBASE up to March 2025. The review included randomized controlled trials (RCTs) that compared HA fillers with controls, such as placebo or alternative treatments, for midface augmentation. Results: A total of fourteen studies were included in the review, and five studies in the statistical analysis. Analysis of five RCTs involving 748 participants showed a higher and significant difference in GAIS responder rates between HA and control groups (RR = 3.27, 95% CI: 2.26–4.75, p = 0.79; I² = 95%). GAIS scores at 4, 8, and 24 weeks demonstrated no notable improvements (all p > 0.05). Adverse events were rarely reported, and there was no significant rise in moderate-to-severe adverse events associated with HA fillers (RR = 1.70, 95% CI: 0.08–34.55, p = 0.73). Conclusions: HA fillers used for midface augmentation are generally safe, they have very high midface augmentation and patient satisfaction value, but they might not provide a notable subjective aesthetic benefit over the other fillers. Clinicians need to take into account patient expectations and refine their techniques, all while recognizing the limitations of existing evidence. Future research should include objective volumetric measurements and extend follow-up durations. Full article

Atmospheric nitrogen input has been a severe challenge worldwide. The influences of N deposition on carbon cycling, loss, and storage have been recognized as a critical issue. This study aimed to assess the immediate responses of soil respiration to different N deposition treatments in radiata pine (Pinus radiata D. Don) and chestnut-leaved oak (Quercus castaneifolia C. A. Mey) plantations within 12 months. N treatments were performed monthly at levels of 0, 50, 100, and 150 kg N ha⁻¹ year⁻¹ from October 2017 to September 2018. Litterfall was collected and analyzed seasonally for its mass and C content. Within the 0–10 cm depth of mineral soil in both plantations, parameters such as total nitrogen, pH, microbial biomass carbon (MBC), organic carbon (OC), and fine root biomass were measured seasonally. Soil respiration (Rs) was determined through monthly measurements of CO₂ concentration in the field using a portable, closed chamber technique. The control plots exhibited the highest Rs during spring (2.96, 2.85 μmol CO₂ m⁻² s⁻¹) and summer (2.92, 3.1 μmol CO₂ m⁻² s⁻¹) seasons in oak and pine plantations, respectively. However, the introduction of nitrogen significantly diminished Rs in both plantations. Moreover, N treatments caused a notable reduction of soil MBC and fine root biomass. Soil microbial entropy and the C/N ratio were also significantly decreased by nitrogen treatments in both plantations, with the most prominent effects observed in summer. The observed decline in Rs in N-treated plots can be attributed to the decrease in MBC and fine root biomass, potentially with distinct contributions of these components in the pine and oak plantations. Our findings suggested that N-induced alteration in soil carbon dynamics was more pronounced in the oak plantation, which resulted in more SOC accumulation with increasing N inputs, while the pine plantation showed no significant changes in SOC. Full article

Mean flow velocities and the corresponding turbulence fluctuation velocities were measured within the suction port of a standard twin-screw compressor using LDV and PIV optical techniques. Time-resolved velocity measurements were carried out over a time window of 1° at a rotor speed of 1000 rpm, a pressure ratio of 1, and an air temperature of 55 °C. Detailed LDV measurements revealed a very stable and slow inflow, with almost no influence from rotor movements except near the rotors, where a more complex flow formed in the suction port. The axial velocity near the rotors exhibited wavy profiles, while the horizontal velocity showed a rotational flow motion around the centre of the port. The turbulence results showed uniform distributions and were independent of the rotors’ motion, even near the rotors. PIV measurements confirmed that there is no rotor movement influence on the inflow structure and revealed complex flow structures, with a crossflow dominated by a main flow stream and two counter-rotating vortices in the X-Y plane; in the Y-Z plane, the presence of a strong horizonal stream was observed away from the suction port, which turned downward vertically near the entrance of the port. The corresponding turbulence results in both planes showed uniform distributions independent of rotor motions that were similar in all directions. Full article

Major depressive disorder (MDD) is one of the most prevalent psychiatric conditions and is characterized by alterations in cortical excitability, network connectivity, and neuroplasticity. Despite significant progress in neuroimaging and neurophysiology, the identification of objective and reliable biomarkers remains a major challenge, limiting diagnostic accuracy and treatment optimization. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) has emerged as a powerful methodology to probe causal brain dynamics with high temporal resolution. This review aims to summarize recent advances in the application of TMS-EEG to MDD, highlighting the transition from traditional TMS-evoked potential (TEP) analyses to more advanced, multidimensional approaches. We reviewed original research articles published between 2020 and 2025 that investigated neurophysiological markers and approaches to MDD using TMS-EEG. Traditional TEP measures provide markers of local cortical responses but are limited in capturing distributed network dysfunction. Emerging approaches expand the scope of TMS-EEG, allowing for the characterization of oscillatory activity, connectivity patterns, and large-scale network dynamics. Recent contributions also demonstrate the potential of computational and multivariate techniques to enhance biomarker sensitivity and predictive value. Taken together, recent evidence highlights TMS-EEG as a uniquely positioned methodology to investigate the neurophysiological substrates of MDD. By linking conventional TEP-based indices with innovative analytic strategies, TMS-EEG enables a multidimensional assessment of cortical function and dysfunction that transcends traditional descriptive markers. This integrative perspective not only refines mechanistic models of MDD but also opens new avenues for biomarker discovery, patient stratification, and treatment monitoring. Ultimately, the convergence of advanced TMS-EEG approaches with clinical applications holds promise for translating neurophysiological insights into precision psychiatry interventions aimed at improving outcomes in MDD. Full article

Given significant water scarcity events in the recent past, water resources management in the Piracicaba River Basin, São Paulo, Brazil, has intensified the adoption of complex measures to meet the population’s water supply demands. This study presents a methodology to optimize reservoir water release while adhering to restrictive rules, aiming to also conserve water. A rainfall–runoff model was utilized alongside a hydrological routing model, incorporating meteorological forecasts for simulation over ten consecutive years. The results demonstrated significant water savings when comparing the optimization scenario with the actual reservoir operation during the same period. The applied methodology reduced water releases up to 66% in comparison to the observed scenario. Overall, the study introduces tools to improve reservoir operation with computational techniques, enriching local water resources management, water security, and decision-making processes, ensuring water security for the São Paulo Metropolitan Region, the most populous region in Brazil. Full article

This paper presents an advanced radio frequency (RF)–direct current (DC) power conversion architecture based on a multistage Cockcroft–Walton topology. The proposed design achieves an enhanced voltage conversion ratio while maintaining superior RF-DC conversion efficiency under low input power conditions. To address the inherent limitations of cascading Cockcroft–Walton topologies with class-F load networks, a novel ground plane isolation technique was developed, which utilizes the reverse-side metallization of the circuit board. A 5.8 GHz two-stage Cockcroft–Walton voltage multiplier rectifier was fabricated and characterized. Measurement results demonstrate that the circuit achieves a maximum output voltage of 7.4 V and a peak conversion efficiency of 70.5% with an input power of only 30 mW, while maintaining stable performance across varying load conditions. A comparison with a two-stage Dickson rectifier reveals that the Cockcroft–Walton rectifier exhibits superior output voltage and conversion efficiency. The proposed architecture delivers significant improvements in power conversion efficiency and voltage multiplication capability compared to conventional designs, establishing a new benchmark for low-power wireless energy harvesting applications. Full article

Structured-light 3D reconstruction is an active measurement technique that extracts spatial geometric information of objects by projecting fringe patterns and analyzing their distortions. It has been widely applied in industrial inspection, cultural heritage digitization, virtual reality, and other related fields. This review presents a comprehensive analysis of mainstream fringe-based reconstruction methods, including Fringe Projection Profilometry (FPP) for diffuse surfaces and Phase Measuring Deflectometry (PMD) for specular surfaces. While existing reviews typically focus on individual techniques or specific applications, they often lack a systematic comparison between these two major approaches. In particular, the influence of different projection schemes such as Digital Light Processing (DLP) and MEMS scanning mirror–based laser scanning on system performance has not yet been fully clarified. To fill this gap, the review analyzes and compares FPP and PMD with respect to measurement principles, system implementation, calibration and modeling strategies, error control mechanisms, and integration with deep learning methods. Special focus is placed on the potential of MEMS projection technology in achieving lightweight and high-dynamic-range measurement scenarios, as well as the emerging role of deep learning in enhancing phase retrieval and 3D reconstruction accuracy. This review concludes by identifying key technical challenges and offering insights into future research directions in system modeling, intelligent reconstruction, and comprehensive performance evaluation. Full article

UV-C radiation has emerged as a germicidal agent against pathogens, particularly following the COVID-19 pandemic. While UV-C effectively reduces cross-contamination in hospitals, it induces photodegradation in polymer devices, potentially damaging and posing risks to patient safety. Therefore, it is crucial to detect the effects of UV-C photodegradation on early stages, as well as the effects of prolonged UV-C exposure. In this study, we investigated the UV-C photodegradation (254 nm, 471 kJ/mol) of isotactic polypropylene homopolymer (PP), commonly used in medication packaging. The impact of UV-C on PP was evaluated through rheology and infrared spectroscopy. Surface energy was measured by the contact angles formed by drops of water and diiodomethane. The effects of photodegradation on the polymer’s morphology were examined using scanning electron microscopy, and the melting temperature and crystallinity by differential scanning calorimetry. Lastly, the effect of UV-C on molecular mobility was studied using ¹H Time Domain Nuclear Magnetic Resonance (¹H TD-NMR). These techniques proved to be valuable tools for identifying the early stages of UV-C photodegradation, and ¹H TD-NMR was a sensitive method to identify the chain branching as a photodegradation product. This study highlights the impact of UV-C on PP photodegradation and hence the importance of understanding UV-C-induced degradation. Full article