Search Results (29,671)

Dynamic harmonic estimation is important for the monitoring and control of power-electronic grids. But the high-precision dynamic harmonic estimation algorithms usually have a heavy computational burden and occupy a large memory space, making them difficult to implement in the embedded platform. Thus, the motivation of this paper lies in providing an estimator with low computational complexity and less storage space consumption. A purely real-valued fast dynamic harmonics estimator is proposed. Firstly, a purely real-valued estimation model is established based on the Taylor series expansion on the time-varying amplitude and phase angle. Secondly, the estimation filter bank is computed in the least-squares sense, and the corresponding estimation error is theoretically analyzed. Finally, the purely real-valued fast dynamic harmonics estimator is designed. The advantage includes significantly reducing the computational complexity and memory space consumption while maintaining high-precision estimation. The testing results show that the proposed estimator can achieve the highest harmonics estimation precision under dynamic conditions. The frequency error, magnitude error, and phase angle error are less than 5 × 10⁻² Hz, 7 × 10⁻¹%, and 8 × 10⁻² degrees, respectively, which verifies the advantage of high-precision estimation. The proposed estimator achieves a computational speed-up of approximately 430, 396, and 330 times compared to the Prony method, ESPRIT method, and iterative Taylor Fourier transform method, respectively. The computational load rate for executing the proposed estimator on the embedded prototype using C6748 DSP for estimating 50 harmonics is approximately only 2.05%, which verifies the advantage of a low computational load rate. Full article

Background: Congenital mesoblastic nephroma represents 3–10% of all pediatric renal tumors. With the advancement of ultrasound diagnostics and magnetic resonance imaging, the diagnosis of this renal neoplasm is increasingly being established prenatally and at birth. It usually presents as a benign tumor, but it can severely affect pregnancy outcomes, contributing to perinatal morbidity and mortality. Lissencephaly belongs to a rare category of neurodevelopmental disorders marked by the absence of a substantial reduction in the typical folds and grooves in the cerebral cortex. The prognosis for patients with lissencephaly is extremely poor, carrying with it a high mortality rate. Case Presentation: We present a case of congenital mesoblastic nephroma (CMN) diagnosed with polyhydramnios at 28 weeks of gestation, which led to preterm delivery at 29 weeks and a fatal outcome for the newborn. Histopathological examination confirmed the diagnosis of CMN along with fetal pachygyria/lissencephaly. The aim of this study is to point out the characteristics and unique correlation between CMN and lissencephaly, and to illustrate the histopathological features of CMN and lissencephaly through an educational example derived from our presented index case. To the best to our knowledge, the association of CMN with lissencephaly has not been described in the literature so far. Conclusions: Outlining the prenatal progression of CMN and the outcome of pregnancies involving fetal CMN and lissencephaly, this case underscores the importance of comprehensive ultrasound examinations, including central nervous system evaluation, to identify potential coexisting anomalies and refine prenatal diagnostic practices. Full article

The planting of fruit trees on sloping land can bring significant benefits to the local economy, but it also causes different degrees of soil and water erosion problems. In this study, we investigated the differences in nutrient migration in slope ditch runoff. In 39 scouring tests, a grass ditch reduced the loss of carbon (C), nitrogen (N), and phosphorus (P) by intercepting runoff. There was a positive correlation between runoff and the loss rate of N and P. The flow affected the retention time of runoff in the ditch, and then changed the dissolved organic carbon (DOC) loss rate in the runoff. The concentration of N and P did not affect the N and P loss rate, but did affect the total amount of N and P lost and the DOC loss rate in the runoff. The addition of organic fertilizer significantly increased the N loss rate in the runoff, and the change rule of the P and DOC loss rate was similar; thus, co-migration might have occurred. To sum up, the importance of the four factors on the migration and loss of C, N, and P in ditch runoff was as follows: organic fertilizer (100%) > fertilizer concentration (74.8%) > ditch type (12.6%) > initial flow (10%). Full article

Quantum Random Number Generators (QRNGs) have been theoretically proven to be able to generate completely unpredictable random sequences, and have important applications in many fields. However, the practical implementation of QRNG is always susceptible to the unwanted classical noise or device imperfections, which inevitably diminishes the quality of the generated random bits. It is necessary to perform the post-processing to extract the true quantum randomness contained in raw data generated by the entropy source of QRNG. In this work, a novel post-processing method for QRNG based on Zero-phase Component Analysis (ZCA) whitening is proposed and experimentally verified through both time and spectral domain analysis, which can effectively reduce auto-correlations and flatten the spectrum of the raw data, and enhance the random number generation rate of QRNG. Furthermore, the randomness extraction is performed after ZCA whitening, after which the final random bits can pass the NIST test. Full article

This study explores the optimisation of rearing substrates for black soldier fly larvae (BSFL). First, the ideal dry matter content of substrates was determined, comparing the standard 30% dry matter (DM) with substrates hydrated to their maximum water holding capacity (WHC). Substrates at maximal WHC yielded significantly higher larval survival rates (p = 0.0006). Consequently, the WHC approach was adopted for further experiments. Using these hydrated artificial substrates, fractional factorial designs based on central composite and Box–Behnken designs were employed to assess the impact of macronutrient composition on bioconversion efficiency. The results demonstrated significant main, interaction, and quadratic effects on bioconversion efficiency. Validation with real-life substrates of varied protein content, including indigestible feather meal, affirmed the predictive model’s accuracy after accounting for protein source digestibility. This research underscores the importance of optimal hydration and macronutrient composition in enhancing BSFL growth and bioconversion efficiency. Full article

Ca and Mg are key constituents in surface water that are essential nutrients and vital indicators of water hardness. Rapid on-site measurement of Ca and Mg concentrations in surface water is important. However, traditional laboratory detection methods are complex and time-consuming, and on-site detection is difficult. In this study, a portable surface water detection method was developed using laser-induced breakdown spectroscopy with a miniaturized spectrometer LIBS and a liquid jet device for sample introduction. The device enables the rapid online in situ measurement of elemental concentrations in the water. The limits of detection for the rapid on-site detection of Ca and Mg in surface water were 11.58 and 2.57 mg/L, respectively. We applied this method to assess the concentrations of Ca and Mg in authentic water samples collected from rivers and ponds. The recovery rates for Ca and Mg were 90.83–101.74% and 93.43–108.74%, respectively. This method is suitable for rapid, on-site, and highly sensitive monitoring of Ca and Mg concentrations in the environment. Full article

Peatlands in OKI Regency are highly flammable during extreme dry seasons and experience flooding during extreme rainy seasons. Research related to peatlands is important to be carried out in disaster mitigation efforts on peatlands. In this study, we aimed to examine the impact of climate anomalies occurring between 2019 and 2022 on the Rainfall (RF), Groundwater Level (GWL), and Soil Moisture (SM) in this region. We analyzed data collected at the CJ2 station to understand the dynamics of these parameters throughout the occurrence of the ENSO and IOD phenomena in 2019–2022. The findings of this study indicate a positive correlation between RF and GWL. Specifically, a higher amount of RF resulted in a stronger correlation. Moreover, the rate of the GWL decline was 0.24 mm/d, while the rate of the SM decline was 0.06%/day. Furthermore, the soil moisture in CJ2 OKI must be maintained at a minimum of 20% to prevent fires. In addition, an empirical equation was derived to express the relationship between GWL (m) and SM (%) as SM = 49,044 GWL + 59,142. The findings of this study provide valuable in-sights for all stakeholders involved in efforts to mitigate the impact of natural disasters on peatlands. Full article

In light of the demographic shift towards an aging population, there is an increasing prevalence of dementia among the elderly. The negative impact on mental health is preventing individuals from taking proper care of themselves. For individuals requiring hospital care, those receiving home care, or as a precaution for a specific individual, it is advantageous to utilize monitoring equipment to track their biological parameters on an ongoing basis. This equipment can minimize the risk of serious accidents or severe health hazards. The objective of the present research project is to design an armband with an accurate location tracking system. This is of particular importance for individuals with dementia and Alzheimer’s disease, who frequently leave their homes and are unable to find their way back. The proposed armband also includes a fingerprint identification system that allows only authorized personnel to use it. Furthermore, in hospitals and healthcare facilities the biometric identification system can be used to trace periodic medical or nursing visits. This process improves the reliability and transparency of healthcare. The test results indicate that the armband functions in accordance with the desired design specifications, with performance evaluation of the main features including fall detection, where a hit rate of 100% was obtained, a fingerprint recognition test demonstrating accuracy from 88% to 100% on high-quality samples, and a GPS tracking test determining position with a difference of between 1.8 and 2.1 m. The proposed solution may be of benefit to healthcare professionals, supported housing providers, elderly people as target users, or their family members. Full article

This research focuses on the removal of emerging contaminants (CEC) present in synthetic aqueous matrices. Azole compounds were selected as CEC of interest due to their persistence and toxicity, particularly the triazole and oxazole groups. These compounds are also trace contaminants listed in the proposed revision of Directive 91/271/EEC on urban wastewater treatment and the 3rd European Union Observation List (Implementing Decision EU 2020/116), highlighting their regulatory importance. The draft Directive includes the implementation of quaternary treatments to achieve the highest possible removal rates of micropollutants. Among the technologies used on a large scale are some advanced oxidation processes (AOP), often combined with adsorption on activated carbon (AC). Laboratory-scale pilot plants have been designed and operated in this research, including UV photolysis and oxidation with H₂O₂ and adsorption with GAC. The results demonstrate that UV photolysis is able to remove all the selected CECs except fluconazole, reaching eliminations higher than 86% at high doses of 31.000 J/m². Treatment by H₂O₂ achieved removals of 4 to 55%, proving to be ineffective in the degradation of persistent compounds when acting as a single technology. Adsorption by AC is improved with longer contact times, reaching removals above 80% for benzotriazole and methyl benzotriazole at short contact times, followed by sulfamethoxazole and tebuconazole. Fluconazole had a mean adsorption capacity at low contact times, while metconazole and penconazole showed low adsorption capacities. Full article

Background/Objectives: This study investigates stress distribution in cancellous bone during pilot drilling for dental implants using the Cowper–Symonds model. Understanding the biomechanical effects of drilling parameters on bone health is essential for optimizing implant stability and longevity. Methods: A finite element analysis (FEA) approach was employed to simulate the pilot drilling process in cancellous bone. A three-dimensional jawbone model was developed from CT scan data, processed using 3D Slicer, and refined with CAD tools. The drilling simulation incorporated a rigid pilot drill and flexible cancellous bone, utilizing explicit dynamic methods. Stress distribution was evaluated for drilling depths ranging from 6 mm to 16 mm, with mesh density and strain rate effects considered to ensure accuracy. Results: The results showed an increase in stress levels with drilling depth, with maximum stress recorded at 16 mm. Initial contact stress was 17.3 MPa, rising to 228.9 MPa at deeper penetration due to increased interaction between the drill and bone. Stress distribution patterns emphasized the critical role of drilling depth and design parameters in mitigating bone damage. Conclusions: This study highlights the importance of optimized drilling protocols and pilot drill design to reduce stress and preserve bone integrity. The findings provide valuable insights into improving implant procedures and demonstrate the utility of FEA as a robust tool for evaluating biomechanical impacts during implant placement. Future research should incorporate cortical bone and thermal effects for a comprehensive analysis. Full article

Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability, minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system. Full article

Fractional vegetation cover (FVC) is an important indicator of regional ecological environment change, and quantitative research on the spatial and temporal distribution of FVC and the trend of change is of great significance to the monitoring, evaluation, protection, and restoration of regional ecology. This study estimates the FVC of the eastern Tibetan Plateau margin from 2000 to 2020 using the image element dichotomous model based on the Google Earth Engine platform using MODIS-NDVI images. It also investigates the temporal and spatial changes of the FVC in this region and its drivers using the Theil–Sen and Mann–Kendall trend tests, spatial autocorrelation analysis, geodetector, and machine learning approaches impact. The results of this study indicated a generally erratic rising tendency, with the Min River Basin (MRB) near the eastern tip of the Tibetan Plateau having an annual average FVC of 0.67 and an annual growth rate of 0.16%. The percentage of places with better vegetation reached 60.37%. The regional FVC showed significant positive spatial autocorrelation and was clustered. Driver analyses showed that soil type, DEM, temperature, potential evapotranspiration, and land use type were the main drivers influencing FVC on the eastern margin of the Tibetan Plateau. In addition, the random forest (RF) model outperformed the support vector machine (SVM), backpropagation neural network (BP), and long short-term memory network (LSTM) in FVC regression fitting. In summary, this study shows that the overall FVC in the eastern margin of the Tibetan Plateau is on an upward trend, and the regional ecological environment has improved significantly over the past two decades. Full article

To investigate the impacts of vegetation change on deep soil water recharge, it is essential to identify the sources of deep soil water and deep drainage. The combination of stable and radioactive water isotopes is an effective method for studying deep vadose zones, though it has been rarely applied in complex gully areas. In this study, we measured δ²H, δ¹⁸O, and ³H in soil water under long-term natural grassland and C. korshinskii on the same slope. Both natural grassland and C. korshinskii plots received deep soil water from rainfall during the rainy season; however, the replenishment thresholds for soil water at depths of 2–10.4 m differed between the two vegetation types, corresponding to rainfall intensities of ≥20 mm and ≥50 mm, respectively. Following the conversion of natural grassland to C. korshinskii vegetation, the rate of soil water storage deficit increased by 46.4 mm yr⁻¹, and deep drainage shifted from 39.6 mm yr⁻¹ to 0 mm yr⁻¹. Deep-rooted vegetation significantly depletes soil water to meet transpiration demands, thus hindering rainfall recharge. These findings have important implications for water and land resource management, especially in areas undergoing significant vegetation changes. Full article

Introduction: Cancer is a highly lethal disease with a significantly high mortality rate. One of the most commonly used methods for treatment is radiation therapy. However, cancer treatment using radiotherapy is a time-consuming process that requires significant manual work from planners and doctors. In radiation therapy treatment planning, determining the dose distribution for each of the regions of the patient’s body is one of the most difficult and important tasks. Nowadays, artificial intelligence has shown promising results in improving the quality of disease treatment, particularly in cancer radiation therapy. Objectives: The main objective of this study is to build a high-performance deep learning model for predicting radiation therapy doses for cancer and to develop software to easily manipulate and use this model. Materials and Methods: In this paper, we propose a custom 3D convolutional neural network model with a U-Net-based architecture to automatically predict radiation doses during cancer radiation therapy from CT images. To ensure that the predicted doses do not have negative values, which are not valid for radiation doses, a rectified linear unit (ReLU) function is applied to the output to convert negative values to zero. Additionally, a proposed loss function based on a dose–volume histogram is used to train the model, ensuring that the predicted dose concentrations are highly meaningful in terms of radiation therapy. The model is developed using the OpenKBP challenge dataset, which consists of 200, 100, and 40 head and neck cancer patients for training, testing, and validation, respectively. Before the training phase, preprocessing and augmentation techniques, such as standardization, translation, and flipping, are applied to the training set. During the training phase, a cosine annealing scheduler is applied to update the learning rate. Results and Conclusions: Our model achieved strong performance, with a good DVH score (1.444 Gy) on the test dataset, compared to previous studies and state-of-the-art models. In addition, we developed software to display the dose maps predicted by the proposed model for each 2D slice in order to facilitate usage and observation. These results may help doctors in treating cancer with radiation therapy in terms of both time and effectiveness. Full article

Gas reservoirs play an increasingly important role in oil and gas consumption and safety in China. To study the problem of erosion and wear caused by gas-carrying particles in the process of gas extraction from gas storage reservoirs, a mathematical model of gas–liquid–solid three-phase erosion of gas storage reservoir columns was established through theories of multiphase flow and particle motion. Based on this model, the effects of the water volume fraction, gas extraction rate, particle mass flow rate, particle size, and bending angle on the erosion location and rate of the pipe columns were investigated. The findings indicate that when the water content volume fraction is low, the water production volume minimally affects the maximum erosion rate of pipe columns. Conversely, the gas extraction rate exerted the most significant influence on the column erosion, showing a power function relationship between the two. When gas extraction volume exceeds 60 × 10⁴ m³/d, the maximum erosion rate surpasses the critical erosion rate of 0.076 mm/a. This coincided with the increased sand mass flow rate, although the maximum erosion rate of the pipe columns remained relatively steady. The salt mass flow rate demonstrated a linear relationship with the erosion rate, with the maximum erosion rate exceeding the critical erosion rate of 0.076 mm/a. The maximum erosion rate of the pipe columns increased, stabilized with larger sand and salt particle sizes, and exhibited an increasing trend with the bending angle. For gas extraction volumes exceeding 46.4 × 10⁴ m³/d and salt mass flow rates exceeding 22 kg/d, the maximum erosion rate of pipe columns exceeds the critical erosion rate of 0.076 mm/a. The conclusions of this study are of some importance for the clarification of the influencing law of pipe column erosion under high temperature and high pressure in gas storage reservoirs and for the formulation of measures for the prevention and control of pipe column erosion in gas storage reservoirs. Full article