In response to the issues of sensor noise and accumulated motion errors that lead to low positioning accuracy and insufficient robustness during large maneuvers or continuous flight of small unmanned aerial vehicles (UAVs) in complex environments, this paper proposes a multi-source and multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors including GPS (Global Positioning System), IMU (Inertial Measurement Unit), and visual sensors to complement the strengths and weaknesses of each hardware component, thereby eliminating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, the method introduces a digital twin UAV data source. By utilizing the Extended Kalman Filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multi-source multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.

Background: The laryngeal mask airway Vision Mask (LMA VM) is a supraglottic airway device (SAD) with vision guidance system. The ideal head and neck position for direct laryngoscopy is known, but the ideal position for placing a LMA is not. The objective of this study is to evaluate and compare the optimal position for placement of a video laryngeal mask airway. Methods: This was a prospective observational, transversal and analytical study of 72 consecutive patients. In the same patient, laryngeal vision was first assessed with the head and neck in the sniffing position and then with the head in the neutral position. Procedures were filmed and performed by the same investigator. Assessment of the laryngeal view was performed using two classifications: modified Cormack-Lehane classification and Brimacombe classification. Device placement was considered adequate when the Cormack-Lehane rating was scored from 1 to 2 and the Brimacombe rating was from 2 to 4. Results: 72 patients were included in this study. In the assessment of the glottis using the Cormack Lehane classification for fiberoptic view, laryngeal visibility was adequate in 64 (88.89%) patients in the neutral position and in 65 (90,28%) patients in the sniffing position (p>0,05). In the fiberoptic view of the glottis, assessed using the Brimacombe classification, laryngeal visibility was adequate in 68 (93%) patients in the neutral position and in 69 (95%) patients in the sniffing position, (p>0,05). There was no statistically significant difference in the rate of success between the sniffing position (70 patients, 97.22% success rate) and the neutral position (67 patients, 93.06% success rate) during the first insertion attempt. Two patients required a second attempt in the sniffing position, while five patients required a second attempt in the neutral position. Conclusion: An adequate sniffing position did not result in a better glottic view than neutral position, and the number and type of additional maneuvers were equal in both positions.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

The study of underwater vehicle wake detection is of significant importance within the field of target detection, localisation and tracking of underwater vehicles. Given that propellers are the propellers of modern ships and underwater vehicles, the propeller wake field represents the principal target source for wake detection in underwater vehicles. The objective of this paper is to propose a method for measuring the wake of an underwater propeller based on a position-sensitive detector. A theoretical model of the relationship between the laser spot displacement and the change of refractive index of the wake field is established on the basis of the principle of laser beam deflection. A prototype experimental setup for underwater propeller wake measurement was constructed based on the aforementioned optical measurement method. Furthermore, the simulation of the propeller wake flow field with strong density stratification and linear density stratification was conducted based on the experimental setup. In the experiment, a three-bladed propeller with a diameter of 39mm was selected, the water tank size was 980mm×380mm×380mm, and the laser acted at a distance of 550 mm from the propeller. The experimental results indicate that the wake dissipation time of the propeller in a strong density-stratified water environment is approximately 800s. Following the stabilization of the wake field density, the laser spot position is observed to be stable at 0.341 mm, with a corresponding refractive index change of 2.99×10-6 RIU. The wake dissipation time of the propeller in a linear density-stratified water environment is approximately 750 s. Once the wake field density has reached a stable state, the laser spot position is found to be 0.441mm, corresponding to a refractive index change of 3.87×10-6 RIU. These experimental results are found to be in general agreement with the simulation results of the propeller wake field. A comparison of the experimental device-based wake measurements with the CTD-based wake measurements reveals a consistent trend.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

The variation of aerosols, especially dust aerosol, in time and space plays an important role in climate forcing studies. Aerosols can effectively reduce land surface longwave emission and re-emit energy at a colder temperature, making estimation of downwelling surface longwave radiation (DSLR) with satellite data difficult. Using the latest atmospheric radiative transfer code (MODTRAN 5.0), we simulate the outgoing longwave radiation (OLR) and DSLR under different land surface and atmospheric profile conditions. The results show that dust aerosol has an obvious “warming” effect to longwave radiation compared with other aerosols, that aerosol longwave radiative forcing (ALRF) increased with increasing aerosol optical depth (AOD), and that the atmospheric water vapor content (WVC) is critical to the understanding of ALRF. A method is proposed to improve the accuracy of DSLR estimation from satellite data for the skies under heavy dust aerosols. The AOD and atmospheric WVC under cloud-free conditions with a relatively simple satellite-based radiation model that yields the high accurate DSLR under heavy dust aerosol are used explicitly as model input to reduce the effects of dust aerosol on the estimation of DSLR. Validations of the proposed model with satellites data and field measurements show that it estimates the DSLR accurately under heavy dust aerosol skies. The root mean square errors (RMSEs) are 20.4 W/M² and 24.2 W/M² for Terra and Aqua satellites, respectively, at the Yingke site, and the biases are 2.7 W/M² and 9.6 W/M², respectively. For the Arvaikheer site, the RMSEs are 23.2 W/M² and 19.8 W/M² for Terra and Aqua, respectively, and the biases are 7.8 W/M² and 10.5 W/M², respectively. The proposed method is especially applicable to acquire relatively high accurate DSLR under heavy dust aerosol using MODIS data with available WVC and AOD data.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMS) perform real-time monitoring functions, but their performances must be carefully assessed. In this work the damage detection performances of a strain-based SHMS, were evaluated on a composite helicopter rotor blade root. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through Finite Element analyses performed on the component. The effects of the loads variability and of damages were decoupled by including a load recognition step in the algorithm. Load recognition was accomplished in different ways: version#1 adopted an Artificial Neural Network (ANN) trained on blades in pristine condition; version#2 was trained also on damaged blades; version#3 adopted a calibration matrix method. Anomaly detection, damage assessment, and localization were performed by using an ANN. Results showed a higher load identification and anomaly detection accuracy for version#1 and version#2 compared to version#3. Gaussian noise reduced anomaly detection and damage assessment performances, and reduced damage localization accuracy for bigger damages. The damage detection performance were obtained for a fibre optic based-SHMS, including the trade-off between probability of detection and false alarm rate for different damage sizes.

This paper proposes a real-time local positioning system (RT-LPs) utilizing a mobile platform equipped with three anchor nodes placed in a right-angle triangle formation for a real-time locating system (RTLS). After deriving an analytic formula to calculate the target position utilizing the measured distances among anchor nodes and the target node, we find that four parameters have an effect on the position error of the target node. The spacing between anchors is a design parameter that must be large enough to the reduce position error. However, the distance from the anchor node to the target node is an operation parameter that must be small enough to reduce the position error. Additionally, the measured ranges among the anchors and the target node have probabilistic distributions with a mean and variance, which are dominant parameters that have effects on the position error. A comparison study was conducted to determine the effects of the parameters of the target position in both a simulation and an experiment, showing rates of approximately 4% ~ 10%. These findings indicate that our simulation can work properly with the proposed method after assuming that the distance error is a Gaussian model.

In the permanent magnet synchronous motor (PMSM) drive system, the unwilling and ear-piercing vibro-acoustics caused by high-frequency sideband harmonics becomes unacceptable in the electric vehicle application. In this paper, a modified space vector pulse-width modulation (SVPWM) technique implemented with hybrid carrier frequency modulation (HCFM) is provided to reduce the sideband current harmonic components and vibro-acoustic responses. The principle and implementation of the proposed HCFM technique are firstly presented, in which the fixed carrier frequency is improved with the sawtooth and random signal-based coupling modulation based on the rotor position. For verification, the experiment tests are carried out on a prototype 12/10 PMSM and microcontroller unit. The effectiveness of the HCFM technique can hence be confirmed, in which the sideband vibro-acoustics reduction shows more effectively than that in conventional random PWM. The proposed approach may provide a new route in noise-cancelling and electromagnetic compatibility for the electric drive powertrain.

Efficient fixed crane position analysis is critical in precast buildings construction to maximize lifting efficiency for precast elements. The primary objective is to minimize the travel distances of fixed cranes, thereby enhancing operational efficiency and cost control. This involves strategically reducing the distances between the transport truck parking, where elements are stored, the fixed crane, and the construction installation point. Despite its significance, existing studies often overlook the dynamic movement efficiency of transport trucks, particularly when dealing with multiple transport trucks and various models of fixed cranes, rendering the opti-mization of this scenario a challenging NP-hard problem susceptible to combinatorial explosion. This study presents a novel mathematical model leveraging the Genetic Algorithm (GA). The model aims to solve optimal model and position of fixed cranes, along with identifying ideal positions for transport truck parking. The study's methodology is validated and demonstrated through a project case study. Comparative analyses between the proposed model and the original planning underscore a noteworthy 6% reduction in both duration and costs when lifting elements directly from transport trucks, as opposed to establishing an on-site stock yard. Furthermore, the developed GA exhibits a substantial improvement in final solutions, achieving a remarkable 30% reduction in both operational duration and costs. This research contributes valuable insights into fixed crane position analysis of precast building construction, providing foundation for enhanced operational efficiency and cost-effectiveness in the industry.

Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on eight subjects while performing 5-minute sitting and walking tasks. Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity.

Accurate power curve modelling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 minutes in a wind farm (WF) consisting of 5 WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

The refractive index of seawater is one of the essential parameters in ocean observation, so it is necessary to achieve high-precision seawater refractive index measurements. A seawater refractive index measurement system based on position-sensitive detectors is an effective means to achieve high-precision measurement of seawater refractive index. However, in the actual seawater refractive index measurement process, the impact of the measurement error caused by the jitter signal obtained from the measurement on the measurement results cannot be ignored. In this paper, we theoretically analyse the causes of the long jitter signals during seawater refractive index measurement and quantify the influencing factors. Through the analysis, it can be seen that the angle between the two windows in the refractive index measurement area of seawater causes a great error on the measurement results, so the angle between the two windows should be controlled within 2.06° in order to effectively reduce the measurement error. At the same time, the factors affecting the measurement accuracy of the position-sensitive detector are analysed, and the factors leading to the reduction of the measurement accuracy of the position-sensitive detector are mainly the background light, the size of the photosensitive surface, and the change of the temperature of the working environment. For the above factors, we add a narrow-band filter with a bandwidth of 0.9nm in front of the photosensitive surface of the position-sensitive detector during the construction of the measurement system, so as to filter out stray light other than the signal light source. On the premise of ensuring the measuring range of the refractive index of seawater, a position-sensitive detector with a photosensitive surface size of 4 mm×4 mm was selected. In order to reduce the temperature variation of the working environment of the position-sensitive detector, the measurement system was partitioned. In order to validate the testing error range of the optimised test system, standard seawater samples under the same conditions were measured. It can be seen that the jitter signal of the measurement system has been reduced from 0.0022mm before the optimisation to 0.0011mm after the optimisation, and the deviation of the refractive index has been reduced by 1.09×10−8RIU. The experimental results show that the refractive index of seawater has been effectively reduced by adjusting the optical path and the structure of the seawater refractive index measurement system.

Hemodynamic relevance of differently located stenoses of the internal jugular veins remains undetermined. It particularly concerns nozzle-like strictures in the upper parts of these veins and stenotic jugular valves located at the end of this vein. This study was aimed at understanding flow disturbances caused by such stenoses. Computational fluid dynamics software, Flowsquare+, was used. We constructed 3-dimensional models of venous outflow, comprising two alternative routes: tube representing the internal jugular vein and irregular network representing the vertebral veins. At the beginning of the tube representing the internal jugular vein, differently-shaped and sized short strictures, representing nozzle-like strictures were built in. At the end of this tube, differently-shaped membranes, representing the jugular valve, were built in. With the use of computational fluid dynamics modeling, we studied how these two obstacles influenced the outflow. We found that the most relevant outflow disturbances were evoked by nozzle-like strictures in the upper part of the internal jugular vein that were either small, long or asymmetrically positioned. Very tight stenotic valves and septum-like malformed valve were equally hemodynamically relevant. These findings suggest that both upper and lower strictures of the internal jugular vein can be of clinical significance.

Multi-rotor drones, a kind of unmanned equipment which is widely used in military, commercial consumption and other fields, has been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell mul-ti-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural lightweight, hydrogen storage methods, energy management strategies. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

The main objective of this work is the assessment of the annual number of hospital admissions for respiratory disease (HARD) due to the exposure to in-healable particulate matter (PM10), within the greater Athens area (GAA), Greece. Towards this aim, the time series of the particulate matter with aerodynamic diameter less than 10μm (PM10) recorded in six monitoring stations located in the GAA, for a 13-year period 2001-2013, is used. Initially, a descriptive statistical treatment of PM10 concentrations took place. Furthermore, the AirQ2.2.3 software developed by the WHO was used to evaluate adverse health effects by PM10 in the GAA during the examined period. The results show that, during the examined period PM10 concentrations present a significant decreasing trend. Also, the mean annual HARD cases per 100,000 inhabitants ranged between 20 (suburban location) and 40 (city centre location). Approximately 70% of the annual HARD cases are due to city centre residents. In all examined locations, a declining trend in the annual number of HARD cases is appeared. Moreover, a strong relation between the annual number of HARD cases and the annual number of days exceeding the European Union daily PM10 threshold value was found.

The forest regeneration phase in Sweden commonly involves mechanical soil preparation followed by planting of Scots pine or Norway spruce seedlings. The prepared soil offers planting positions with different properties, including decreased damage by pine weevil (Hylobius abietis L.). Nitrogen fertilization can be applied at the time of planting to aid establishment of the seedlings. In this study we compared the effect of organic nitrogen-fertilization for different planting positions, and seedling sizes on early survival and growth of Scots pine and Norway spruce seedlings. The main planting positions were capped mound, hinge, and mineral soil. Seedlings planted close to organic material were categorized as “low quality positions”, since proximity to organic material increase pine weevil attraction. Higher mortality rates related to pine weevil damage were recorded for seedlings planted in the low-quality positions, regardless of seedling size or N-fertilization. Pine weevil attack rates increased with increasing seedling size. Growth was in general lowest in mineral soil positions. The effect of organic N-fertilization on growth was positive for spruce regardless of planting positions and seedling size, while depending on planting position and seedling size for pine, indicating that effects of organic N-fertilization depend on seedling species, -size and planting position.

To address the issue of reduced orbit estimation accuracy resulting from discrepancies between in-orbit measured geomagnetic field values and IGRF model values in autonomous orbit estimation of microsatellites based on geomagnetic field measurements, this study introduces an adaptive cubature H-infinity filtering algorithm. Firstly, it presents the low-altitude orbital dynamics model and geomagnetic field measurement model for microsatellites. Subsequently, within the adaptive cubature H-infinity filtering algorithm, a spherical simplex rule and a second-order Gaussian-Legendre quadrature criterion are employed to compute the spherical surface integral and radial integral. Additionally, a spherical simplex-radial quadrature rule is proposed to enhance the approximation accuracy of nonlinear Gaussian weighted integrals. Furthermore, by integrating extended H-infinity filtering based on game theory with the cubature quadrature rule, an inverse proportional relationship between constraint level and filtering innovation is established. This allows for adaptive adjustment of the constraint level to enhance robustness against model errors. Finally, through semi-physical simulation experiments, it is demonstrated that compared with traditional algorithms, the proposed algorithm improves autonomous orbit estimation position accuracy of microsatellites by 7.49%, while also enhancing velocity accuracy by 3.1%, thus validating its effectiveness.

This study investigates the impact of rotor spacing on the aerodynamic performance of a coaxialcopter. It has been observed that a small distance between two rotors can result in significant interaction between the propellers, consequently affecting the rotor's aerodynamic efficiency. Exploiting this characteristic, the present research introduces a coaxialcopter with variable rotor spacing. Employing finite element numerical simulations, we assess the aerodynamic behavior of this novel configuration. Through comprehensive measurements and analysis of its aerodynamic performance across varying rotor spacings from 0.1R to 1R, we validate the effectiveness of a rotor spacing control strategy for enhancing takeoff maneuvers. Our numerical simulation results reveal that the performance characteristics of both upper and lower rotor converges toward that of a single rotor as pitch ratio increases, along with the reduce of their thrust fluctuations and aerodynamic performance periodicity. Considering stable power consumption patterns and endurance performance, we analysis the interrelations binding of pitch distance of the rotors, rotational speed, and pitch angle, vis-à-vis the thrust coefficient and power coefficient. Through parameter optimization method, we demonstrate that adjusting rotor spacing offers a practical means to enhance payload capacity without increasing power input, thereby improving the efficiency, which validates the practicality and efficacy of the parameter optimization approach. Furthermore, optimizing rotor spacing for specific operational scenarios enhances overall aerodynamic performance, suggesting a viable flight control strategy for takeoff and landing conditions.

Time estimation was investigated in 24 healthy adults, 12 women and 12 men, before and after an exhaustive exercise. We compared the capability of estimating time intervals in the range from 1 to 5 s using tasks requiring mental count and tasks that did not allow the mental count. Time estimation and blood lactate levels were evaluated before and at the end of the exercise. We found that the perception of time intervals between 1 and 5 s was affected at the end of the exercise. The observed effects, associated with a significant increase in blood lactate levels, were different in the two types of time estimation used in the present study. In experimental conditions in which participants had to evaluate duration of the time interval by mental counting, at the end of exercise a significant reduction in the overestimation of time made at rest was observed. On the other hand, when participants had to assess the difference in duration between two events without the possibility to mentally count, at the end of the exercise a significant deterioration in performance was observed. In both cases, no gender differences were seen.

Introduction: Hypertension is a serious public health problem in Morocco. The objective of this pilot study was to estimate the salt intake of Moroccan adults by measuring 24-hour urinary sodium excretion. Methods: 132 participants were recruited from the central region of Morocco. Sodium, potassium and creatinine levels were measured using the 24-hour urine samples. Additional data included anthropometric measurements and socio-demographic questionnaire. Results: The daily sodium, potassium and creatinine excretions were 2779.1±1334.9 mg, 1350.0±642.8 mg and 820.3 ± 381.2 mg, respectively. Data analysis revealed that 71.2% of the participants had a daily sodium intake more than 2000 mg (5g of salt) recommended by the WHO. The mean of potassium excretion was 1350.0±642.8 mg /day, this average was lower than 3.51g per day recommended by the WHO. Conclusion: Public health measures to reduce sodium and increase potassium consumption in order to decrease the population’s risk of high blood pressure and heart disease are recommended.

Employing remote sensing techniques to evaluate crop residue cover (CRC) enables rapid mapping of tillage practices over large cropland areas, which has significant practical implications for monitoring the implementation of conservation tillage practices. This research utilized Sentinel-2 MSI and Landsat-8 OLI remotely sensed data, various spectral indices were calculated and the datasets were correlated with the field-measured CRC data. Selected spectral indices, highly significantly correlated, were chosen to build a correlation model between the indices and the CRC. Considering Sentinel-2 MSI and Landsat 8 OLI remotely sensed data variations in spectral and spatial scales, the accuracy of CRC models was evaluated using R2 and RMSE values. From the results, it's evident that all six spectral indices, including STI, NDTI, SRNDR, NDI5, NDI7 and NDSVI, registered correlation coefficients with the CRC exceeded 0.3 in the study area. STI and NDTI showed a stronger correlation to CRC, with coefficients of 0.810 and 0.803, respectively. For the Landsat 8 data, the lowest correlation obtained was for NDSVI with the Landsat 8 data with a correlation coefficient of 0.358. Applying the linear regression technique, STI and NDTI obtained higher accuracy compared to other indices in the Sentinel-2 MSI data. For NDTI and STI, The R2 values were 0.650 and 0.665, respectively, while their corresponding RMSE values were 3.96% and 4.03%. The highest model accuracy was obtained for the STI and NDTI constructed from the Landsat 8 OLI data (R2 values 0.41 and 0.40; RMSE values 5.25% and 5.27%, respectively). The CRC model built using partial least squares regression (PLSR) exhibited greater precision when applied to the Sentinel-2 MSI data (R2 value 0.894; RMSE value 2.18%) in comparison to the Landsat 8 OLI data (R2 value 0.695; RMSE value 3.70%). PLSR has the potential to enhance the precision of the model. In addition, the study area shows that the estimation of the CRC is better in the one-dimensional linear regression model and the PLSR model created with the Sentinel-2 MSI optical data compared to the Landsat 8 OLI optical remotely sensed data. The validation suggests that the CRC estimation in the study area is more precise when using the model relying on Sentinel-2 satellite imagery rather than the Landsat 8 OLI imagery.

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of dual-phase-lag heat conduction model with the Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag-Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms namely random forest (RF), partial least squares (PLS), ridge regression (RR), K-Nearest Neighbor (KNN) and eXtreme Gradient Boosting Decision Tree (XGboost) were utilized for multi-sensor data fusion, and three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average) for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB+MS+Texture+TIR combination demonstrating the highest fusion performance (R2=0.660, RMSE= 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the prediction accuracy of wheat yield. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE= 0.668 t ha-1). It significantly outperformed both the second-layer ensemble methods of Stacking (R2= 0.668, RMSE= 0.673 t ha-1) and feature-weighted (R2= 0.667, RMSE= 0.674 t ha-1), thereby exhibiting superior predictive capabilities. This finding demonstrated that the third-layer ensemble method not only augments the predictive ability of the model but also fine-tuned the accuracy of wheat yield prediction through the employment of simple average ensemble learning. Consequently, it offers a novel perspective for crop yield prediction and breeding selection.

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

With the progression of AI, embedding advanced AI technologies into small robotics and mobile devices has become essential, driving research towards lightweighting AI models. Our study enhances the EPINET depth estimation model for light field images, aiming for compactness and faster inference while preserving accuracy. We conducted two-step experiments aimed at enhancing inference efficiency: Initially, by adjusting input streams and convolution layers, we simplified the CNN model, achieving faster inference times at the cost of reduced accuracy. To address this reduction in accuracy, we then applied knowledge distillation, allowing the simplified model to learn from the original model’s more complex patterns. In our quantitative experiments using two error metrics, MSE (Mean Squared Error) and BadPix, we identified optimal knowledge positions and evaluated the required complexity for the student model. As a result, our method improved MSE by 21% and BadPix by 14% compared to training without it. Furthermore, the student model achieved an inference speed 13% faster than the teacher model and surpassed its accuracy by 10% in MSE. Additionally, we demonstrated that repeatedly applying our approach could further enhance both model compactness and accuracy.

Purpose: The deterioration of oral function has been getting much attention as “Oral frailty”. This study evaluated the change in tongue pressure, one of the objective items of oral frailty, to examine the relationship between body position and tongue pressure. Methods: This study was prospective, observational study in a single-center. The participants were categorized by Clinical Frailty Scale (CFS). Tongue pressure was measured in the following positions: dorsal, sitting, and sitting with plantar grounding. Differences in tongue pressure between CFS and between body positions were statistically analyzed. Results: A significant decrease in tongue pressure was demonstrated in CFS4 compared to CFS3. Furthermore, in CFS5 and CFS6, tongue pressure decreased in the sitting po-sition compared to that in the dorsal position and recovered to the dorsal level with plantar grounding. Conclusions: Tongue pressure decreased as the progression of frailty. It decreased by sitting, and this decrease was alleviated by plantar grounding.

With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most of the cellular systems such as Bluetooth indoor positioning system due to multipath and non-line-of-sight (NLOS) propagation, the indoor positioning accuracy is severely affected. In this paper a comprehensive algorithm is investigated that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates obtained using particle filtering, which allows us to explore new optimised methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by about 25.7% on average.

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ET_a) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ET_a over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ET_a values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ET_a is limited to water surplus areas only and in wet season, ET_a was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ET_a over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ET_a estimated for the whole estate were 107 Mm³, 140 Mm³, and 178 Mm³ using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

A study on flywheel geometries is presented here with the use of finite element modelling (FEM) simulations. The study analyses the stress behaviour of flywheel rotors subject to the rotational speed that creates the maximum stress that the flywheel can support, and then calculates the rotational energy mass ratio (energy density) for each geometry. The obtained geometry was Gaussian section for the Flywheel rotor. The used material for the simulations is carbon fibre Hexcel UHM 12000. The best results were a rotational speed of ≈279.000 rpm and rotational energy density of ≈ 440 Wh/kg for a single disk, but this keeps the total energy low. To increase the total energy, Flywheels with 2 and 3 rotors were analysed. Then the stability under rotation of these Flywheels were analysed, some instability was found and a solution was presented.

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM-IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Run Final (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001-2020. The Asfezari gridded precipitation data which are developed using a dense of ground-based observation were utilized as the reference dataset. The findings indicate that the IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All the three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggest that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

Two beneficial characteristics of aircraft generators are having armature windings and PM excitation sources placed on the stator side (maintenance and thermal management) and having a clear-cut rotor without permanent magnets or excitation windings (vulnerable at high speeds due to associated centripetal mechanical stresses). Within this framework, a doubly-salient permanent magnet (DSPM) generator was conceived by optimizing the stator size and rotor structure to minimize the torque ripple and maximize the RMS voltage value per turn of each generator phase. A Multi-Objective Design Strategy based on a 2D finite element analysis was used, being set to have characteristics closest to optimal for a Boeing 767 turbine, that is, the necessary electromotive force for a required power of 90 kW at 3000 rpm, feeding a simplified Boeing 767 electrical power distribution system. The results show that the machine could not deliver the required power at 3000 rpm since the 2D finite elements analysis demonstrates that the 2D model gives optimistic results when compared with the 3D FEM model. However, with a 3D finite element analysis of the machine feeding the aircraft load, it was seen that the machine’s efficiency is 92%, suggesting this machine can be a plausible solution.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

1) Landsat operational land imager (OLI) data and consequent laboratory measurements were used to predict Chlorophyll-a (Chl-a) concentration and the trophic states for an inland lake within the East Kolkata Wetland, India; 2) The most suitable band ratio was identified by performing Pearson correlation analysis between Chl-a concentrations and possible OLI band and band ratios from the study points; 3) The results showed highest correlation coefficient from the band ratio OLI5/OLI4 with an R value of 0.85. The prediction model was then developed by applying regression analysis between the band ratio OLI5/OLI4 and Chl-a concentration of the study points. The reflectance ratios of the validation points were given as input on the prediction model and the model output was considered as predicted Chl-a values of the validation points to check the efficiency of the prediction model. The regression model between laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points revealed a high correlation with an R² value of 0.78. Trophic State Index (TSI) of the lake was also calculated from laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points. The study presented a high correlation of TSI determined from predicted data with TSI from laboratory reference data (R = 0.88). The TSI values of the lake ranged from 65 to 75 which indicate that the lake is appeared to be eutrophic to hypereutrophic conditions. 4) This empirical study showed that Landsat 8 OLI imagery can be effectively applied to estimate Chl-a levels and trophic states for inland lakes.

The stock beta coefficient literature extensively discusses the proper methods for the estimation of beta as well as its use in asset valuation. However, there are relatively few references with respect to the appropriate time horizon that investors should utilize when evaluating the risk-return relationship of a stock. We examine the appropriate time horizon for beta estimation differentiating our results by sector according to the Industry Classification Benchmark. We employ data from the NYSE and we estimate varying lengths of beta employing data from 30 to 250 trading days. The constructed beta series is then examined for the presence of breaks using the endogenous structural break literature. Results show evidence against the use of betas that employ more than 90 trading days of data provisional to the sector under study.

Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (R_h) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (R_eco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC [1]. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

In this paper, we present a multi-task model that predicts disparities and confidence levels in deep stereo matching simultaneously. We do this by combining its successful model for each separate task and obtaining a multitask model that can be trained with a proposed loss function. We show the advantages of this model compared to training and predicting disparity and confidence sequentially. This method enables an improvement of 15% to 30% in the area under the curve (AUC) metric when trained parallel rather than sequential. In addition, the effect of weighting the components in the loss function on the stereo and confidence performance is investigated. By improving the confidence estimate, the practicality of stereo estimators for creating distance images is increased.

This interdisciplinary investigation of the human remains of Balduin Helm, one of the most important abbots of Fürstenfeld monastery, provides novel information on that historic individual. This is particularly interesting since Balduin, during early 18th century, was involved in the renovation of this large Bavarian monastery. Metastatic prostate cancer was found as evidenced by multiple mixed osteolytic-osteoblastic bone lesions in all available vertebral bodies, fragments of both hemipelvises and isolated metastases in skull bones. Distribution, radio- and histomorphology, and especially the immunohistochemical detection of prostate-specific antigen in those metastases, definitively confirm this diagnosis. Further investigation, especially by stable isotope analysis, showed a balanced high-level diet with considerable contribution from animal protein and significant freshwater fish. These additional findings suggest a significant radiocarbon reservoir effect as an explanation for a “too old” radiocarbon dating. Finally, the obviously high-level protein diet may have contributed to the tumorigenesis which caused the death of the abbot at an advanced age.

Forest ecosystems make a greater contribution to carbon (C) stocks than any other terrestrial ecosystem. To understand the role of regional forest ecosystems in global climate change and carbon exchange, forest C stock and its spatial distribution within the small (2,300 km2) Liuxihe River basin were analyzed to determine the different contributors to the C stock. Forest C stocks were quantified by measuring the biomass of trees, understory vegetation, litter and roots, as well as soil organic C, using data from field samples and laboratory experiments. The results showed that forests stored 38.04 Tg C in the entire basin, with secondary and planted forests accounting for 89.82% and 10.18%, respectively, of the stored C. Five types of forests, a subtropical evergreen broad-leaved forest, a subtropical coniferous and broad-leaved mixed forest, a subtropical coniferous forest, a timber forest, and a non-wood forest, stored 257.55 ± 15.01, 218.92 ± 9.59, 195.24 ± 18.29, 177.42 ± 17.55, and 117.86 ± 6.04 Mg C ha−1, respectively. In the forest ecosystem C stocks of the basin, soils averagely contribute about 73.78%, not including root underground biomass. It provides a comprehensive method for forest ecosystem carbon investigation and forest management in small basin scale.

With the increasingly stringent airworthiness standards, the noise generated during the rotorcraft flight is gradually attracting people’s attention. It widely operated helicopters at low altitudes because of their maneuverability. The way to reduce the noise caused by the complex airflow of the helicopter rotor system has progressively become a hot topic for researchers. Using a hybrid acoustic analysis method, this paper investigates the improvement of the noise and thrust of the helicopter’s tail rotor through the tail rotor structural parameters. For the basic model, the turbulence simulation is performed using an incompressible detached eddy simulation (DES) method, and the Lighthill acoustic analog equation is calculated using the finite element method (FEM). We verified the accuracy of the method through wind tunnel tests. We chose a series of structural parameters for sound simulation and fluid simulation calculations. The results indicate that the modified tail rotor noise reduced by 16.5 dBA and the total thrust increased by 19.9% from the prototype model. This work can enhance the duct tail rotor design to improve aerodynamic and aeroacoustic performance.

Ultrasound (US) scans of the inferior vena cava (IVC) provide useful information on the volume status of a patient. However, their investigation is user-dependent and prone to measurement errors. An important technical problem is the objective difficulty in studying a very compliant blood vessel like IVC, which makes large respirophasic movements and shows a complicated three dimensional geometry. Using bi-dimensional (2D) B-mode views either in long or short axis has improved the characterization of IVC dynamics compared to a measurement along a single direction (M-mode). However, specific movements of the IVC can challenge also the information provided by these 2D sections. Thus, these two orthogonal views, provided by an US system in X-plane, are here integrated by an innovative method. It is tested on simulated videos of the IVC performing complicated movements, that are compensated by the new method, overcoming the biased measurements provided by 2D scans. The method is then applied on example experimental data.

Objective: (1) Describe the current literature on the relationship between EDS and SEP and (2) provide recommendations for consideration of SEP in sleep medicine and biomedical research. Methods: Databases Medline/Pubmed, Web of Science, Google scholar and Scopus were screened using PRISMA guidelines and 19 articles were included in the final synthesis. Results: All studies were cross-sectional. Among these studies, 21.05% (n = 4) are focused on children and adolescent and the lasting 88.95% (n = 15) focused on adults and old people. Age ranged between 8 and 17 years old for children/adolescent and ranged from 18 until 102 years old for adults. Main SEP measures presented in these studies were education, income, perceived socioeconomic status and employment. Sample size in these studies varied from N = 90 participants until N = 33865 participants. Overall, a lower educational level, a lower income and full-time employment were associated with EDS. EDS symptoms are prevalent in women, especially those with a low income or no job; and children and adolescents with difficult living conditions or people working part-time reported more sleep disturbances. Conclusions: SEP is already considered as an important determinant for many health outcomes, but if SEP is embedded in experimental design in psychosomatic research, biomedical research and clinical practice as a constant variable regardless of outcome; it will move forward future investigations.

Cochlear implantation is the therapy used for patients with severe to profound sensorineural hearing loss. For the success of the surgery, it is important that each surgical step is performed with meticulous precision, starting from the correct patient’s position on the operating table. In elderly or obese patients, this can be difficult to achieve due to cervical rigidity. In these cases, the orthopedic position is an excellent choice to perform a mastoidectomy accurately, ensuring a safe procedure and avoiding unpleasant complications.

Software project estimating (SPE) is a powerful but necessary step in the production of projects for software. Software project estimating includes several components, estimating software time, software resources, software costs, and software effort estimation (SEE). SEE seeks to estimate the number of "hours-person" and "months-person" of work required for developing or managing a software program. During the initial phase of the development of software, future efforts are impossible to predict. To estimate effort, many deep learning (DL) and machine learning (ML) models were previously created. For estimate, single-model techniques and ensemble methods were investigated in this research. Ensemble methods involve a combination of multiple separate models. Bagging, boosting, and voting were the ensemble methods looked into for estimation. Datasets considered for estimation were COCOMO81, COCOMONasa I, COCOMONasa II, Albrecht, Maxwell, China, Desharnais, Desharnais_1_1, Kitchenham, Boehm, and Belady. The evaluation criteria used MAE, RAE, RMSE, RRSE, and CC. The result from previous studies is that the total size of the set of data utilized is small in predicting the effort, as the largest size of the set of data used is the China dataset containing only 499 projects. So, in this study, the dataset has been merged to increase the total size of the set of data and increase the accuracy of prediction. The results indicated that the suggested merged dataset increased effort estimation accuracy.

With the widespread use of electric machines there is a growing need to extract information from the machines to optimize their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of an squirrel cage induction motor rotor physical variables. The system comprises: circuit to acquire desirable rotor variable(s) value(s) and send it to the computer; a rectifier and power storage circuit that besides converting an alternating current in continuous also store energy for a certain amount of time to wait the motor engine shutdown; and magnetic generator that harvest energy from rotating field to power the circuits mentioned above. The embedded system is set on the rotor, making it difficult to power the system because it is rotating, problem solved with the construction of the magnetic generator eliminating the need of using batteries or collector rings and send data to the computer using a wireless NRF24L01 module. For the proposed system validation a temperature sensor (DS18b20) was used, variable known as the most important when identifying the need for maintenance and control systems. Tests were made getting satisfactory results proving the viability of using sensors on the rotor.

Inflammation is a cause of number of disorders. Many anti-inflammatory drugs are available to cure and prevent inflammation and its related disorders. Some of the drugs are from plant and animal origin. Among animals snake venom is also utilized as treatment option of certain disorders. In the current study the venom of N. oxiana from Northern areas of Pakistan is assayed to determine its anti-inflammatory activity. The Naja oxiana is also called central Asian cobra is endemic to Central Asia. Naja oxiana belong to family Elapidae and it is highly venomous snake. The venom has many enzymatic proteins as well as nonenzymatic proteins that is neurotoxins. The venom is lethal having very high mortality rate and the death may occur due to respiratory failure in 45 minutes to 24 hrs depending upon the nature and amount of venom. As the venom is highly toxic so initially after venom milking its protein estimation was done and toxicity profile and LD50 was determined to find out its therapeutic dose at which anti-inflammatory activity was observed. The toxicity and anti-inflammatory activity was determined on mice model. Inflammation was induced by carrageenan induced peritonitis method. The inflammatory markers ROS, TNF-α and IL-β were determined by ELIZA in peritoneal fluid. The LD50 of crude venom, fraction 1 and fraction 2 was found as 34 µg/kg, 7.4 µg/kg and 416 µg/kg respectively. Anti-inflammatory activity crude venom, fraction 1 and fraction 2 was found at dose 19 µg/kg, 1.8 µg/kg and 267 µg/kg respectively. The levels of inflammatory markers reduced after therapeutic dose administration of venom and its fractions that indicates that it can be utilized as anti-inflammatory agent and so can be effective in various disorders.

Brushless Synchronous Machines (BSM) are replacing conventional synchronous machines with static excitation in generation facilities due to the absence of sparking and lower maintenance. However, this excitation system makes challenging measuring electric parameters in the rotor. This fact highly difficult the detection of ground faults, which are the most common type of electrical fault in electric machines. In this paper, a ground fault detection method for BSM is proposed. It is based on an inductive AC/DC rotating current sensor installed in the shaft. In case of a ground fault in the rotating parts of the BSM, a fault current will flow through the rotor’s sensor, inducing voltage in its stator. Analyzing the frequency components of the induced voltage the detection of a ground fault in the rotating elements is possible. The ground faults detection method proposed covers the whole rotor and discern between DC and AC sides. To corroborate the detection method, experimental tests have been performed using a prototype of this sensor connected to laboratory synchronous machines achieving satisfactory results.

In accordance with problems such as difficulty in obtaining aerodynamic parameters of a quad-rotor model, the change of model parameters with external interference affects the control performances, an aerodynamic parameter estimation method and an adaptive attitude control method based on LADRC are designed. Firstly, the motion model, dynamics model and control distribution model of quad-rotor are established by using the aerodynamic and Newtonian Euler equations. Secondly, the identification tool CIFER is used to identify the aerodynamic parameters with large uncertainties in frequency domain and a more accurate attitude model of the quad-rotor is obtained. Then an adaptive attitude decoupling controller based on LADRC is designed to solve the problem of poor anti-interference ability of the quad-rotor, so that the control parameter b0 can be automatically adjusted to identify the change of the moment of inertia in real time. Finally, a semi-physical simulation platform is used for simulation verification. The results show that the adaptive LADRC attitude controller designed can effectively estimate and compensate the system's internal and external disturbances, and the tracking speed of the controller is faster and the precision is higher which can effectively improve system's anti-interference and robustness.

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. Fruit Harvest Helper seeks to simplify their process. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. The old and new applications were designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app utilizes a color ratio-based image segmentation algorithm implemented in OpenCV C++ to detect apples in apple tree images selected for processing. The results of testing the algorithm with a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture, nearing readiness for farmers to use in yield monitoring and farm management.

During an interdisciplinary study of the mire “Pod Małym Śnieżnikiem”, a very old specimen of the Norway spruce (Picea abies L.) was encountered. The aim of the present work was to perform a detailed examination of this tree, to compare it to other spruce trees on the mire, and to provide support for establishing protection for this tree stand. Tree-ring cores were sampled at 1.3 m above ground using Pressler borer, in two field campaigns: in June and July 2023, the latter campaign aiming to find the oldest trees. A total of 46 trees were sampled, yielding 84 measuring radii. Tree ring widths were measured down to 0.01 mm under a stereomicroscope. The oldest sampled tree yielded a total of 370 tree rings in the two radii, representing the period 1653–2022. The average tree ring width for TM15 equals 0.33 mm/year, and shows low values (on average 0.19 mm/year) for the period 1742–1943, i.e., during the Little Ice Age cooling. TM15 does not stand out from other trees from the population with respect to height or trunk diameter. A comparison of the age of this tree to the oldest spruce trees in Poland indicates that it is one of the longest living specimens of this species. Considering the natural character of the stand, the remaining flora and the peat-forming processes taking place within the mire “Pod Małym Śnieżnikiem”, we argue that the mire should become protected by the law as soon as possible in order to preserve this valuable high-mountain habitat.

In this study, the determination of static E50 and υ parameters of rock materials was investigated using P-S wave velocities and Shore hardness (SH), non-destructive measurement methods. We used multiple linear regression (MLR), multiple non-linear regression (MNLR) and artificial neural network (ANN) models to estimate and determine these parameters. When the models defined by MLR, MNLR, and ANN were compared to the R2 values, ANN models that estimate E50 and υ parameters of rock materials using non-destructive methods (Vp, Vs, Vp/Vs, d, and SH) could be determined with higher accuracy than MLR and MNLR models. The study’s originality is based on the fact that ores such as galena, chromite, and barite have been studied for the first time on rock mechanics, and in this respect, it offers an innovative perspective. In addition, the use of all non-destructive measurement methods, Vp, Vs, and Shore hardness tests, also increases the originality of the study.

VE is a powerful technique for analyzing a design with a focus on functionality, reducing costs, and increasing value. However, in order to proceed with the design of VE, the theoretical basis for the VE target selection process, which must be preceded in the preparation stage, is lacking, and as a result, it is difficult to verify the most effective target selection. Thus, a theoretical basis is needed. In this study, we intend to quantify the WORTH value of the COST TO WORTH technique and present an objective model of the target selection method using the derived Value Index (VI). For this purpose, cost worth (WORTHC) was calculated based on the performance data of 1,008 cases of 15 building types provided by the Domestic Public Procurement Service and the share of 83 detailed processes in 7 fields. Then, a survey was conducted targeting the ordering companies, and the final worth (WORTHF) was calculated by multiplying the derived customer value (WORTHV ) weight for each detailed work type. We calculated the Value Index (VI) by comparing COST and WORTHF for each detailed construction type of the VE project and developed a model to select construction types that exceed the VI standard value derived using the standard error of the population. Both cost experts and non-cost experts can use the developed model to specify the VE target selection criteria in order to easily select the key target, objectively sort the values that serve as the basis for calculating the value index and make it possible to more easily select the VE target selection criteria based on theoretical evidence. Also, the model can be used to obtain effective detailed design VE results.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer(CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

According to the IPCC guidelines, CO₂ emissions from biomass should be excluded from the entire amount of CO₂ emissions when calculating CO₂ emissions and should be separately reported due to the “carbon neutrality.” Sewage sludge is one of the representative biomass fuels. And it is mixed with fossil fuels in terms of greenhouse gas reduction or is used as fuel to replace fossil fuels by itself. According to the results of this study, biomass contents of both the sewage sludge and the sewage sludge incineration exhaust gases did not amount to 100%. At present, in many countries(South Korea, Japan, and Germany), when calculating greenhouse gas emissions from sewage sludge incinerator, all CO₂ emissions from sewage sludge are judged to be biomass and only those greenhouse gas emissions that correspond to Non-CO₂ gases are calculated as greenhouse gas emissions. However, since, according our results, the content of sewage sludge is not 100% biomass, if CO₂ emissions are excluded according to the existing greenhouse gas emission calculation method, the amount of emissions may be underestimated. Therefore, to accurately calculate greenhouse gas emissions from sewage sludge incinerator, CO₂ emissions should be calculated in consideration of the fossil carbon contents of sewage sludge.

The current study compared the goodness of fit and estimation precision of Bayes and maximum likelihood (ML) structural equation modeling across sample sizes. Data consisted of Markov Chain Monte Carlo generated samples of 50, 75, 100, 125, 150, 200, 250, 500, 750, 1000, and 1500 observations. The Bayes and ML methods were used to estimate a structural equation model with 40 parameters, including 12 continuous observed indicators, four latent variables, and three structural paths. Analyses were conducted with the Mplus 8.0 statistical software and were replicated 500 times with each of the 22 conditions (11 sample sizes x 2 estimation methods). Indices of model fit were the Bayes information criterion, the Tucker-Lewis index, the comparative fit index, the root mean square error of approximation, the posterior predictive p-value (for Bayes models), and the ꭓ2 test (for ML models). Results showed that as the sample size increased, both estimation methods exhibited improved model fit and enhanced estimation precision, and both methods performed well with samples of 200 or larger; however, ML demonstrated a marginally superior fit with smaller samples. These findings were not consistent with previous research and suggest that ML remains a better choice with continuous data and small samples.

The potential of generalized machine learning models developed for crop water estimation was examined in the current study. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) are three ensembled machine learning models that were developed using all of the data from a single location from 1976 to 2017 and then immediately applied at eleven different locations without the need for any local calibration. For the test period of January 2018 to June 2020, the model's capacity to estimate the numerical values of crop water requirement (Pen-man-Monteith (PM) ETo values) was assessed. In comparison to the GBM and RF models, the XGBoost model outperformed them both marginally and significantly. The estimate's weighted standard error was smaller than 0.85 mm/day, and the model's effectiveness varied from 96% to 99% across various locations. The model's strong performance was indicated by the decreased noise-to-signal ratio. A real-time water management system at the regional level can be seamlessly linked with this type of model due to its accuracy in estimating crop water requirements and its capacity to generalize.

Interior permanent magnet motors (IPMSMs) are extensively used as traction motors today because of their exceptional torque and power density, and wide constant power operating range. Under real-world usage, an IPMSM rotor undergoes varying electromagnetic, thermal, and mechanical loads. Under such conditions, fatigue life-based design criteria should be utilized over stress-based design criteria to ensure the structural integrity of the rotor. Moreover, the driving dynamics can change the rotor temperature continuously, which affects the electromagnetic, mechanical, and fatigue properties of rotor material. This paper proposes a robust thermo-mechanical rotor fatigue simulation workflow considering the significant loads acting on an IPMSM rotor and the temperature variation throughout a drive cycle. It discusses an accelerated fatigue life estimation approach based on peak valley extraction to reduce the simulation time significantly for stress and fatigue analysis. Then, it presents a method for stress-life curve generation for a variable loading. It also presents a sensitivity study with a median S-N curve, and a 90%-reliability 95%-confidence (R90C95) S-N curve.

An entrepreneur is a business-able physical person who performs activities to gain the profit and who is registered according to law. Starting from the national and theoretical and legal solutions and court practice from comparative law, the authors analyze the concept and the legal position of an individual entrepreneur noticing the problems and inconsistencies in legal regulations. The authors of the work make a few conclusions and suggestions: 1) terminology is not coordinated with legal terminology from comparative law. In our law, the legal term is “entrepreneur”, which is a too wide and unspecified term because in economic profession this term represents the genus term for individual and collective entrepreneurship; 2) analyze all forbidden activities for entrepreneurs, judge the reasons pro et contra and work on eliminating prohibitions and favouring legal entities; 3) set by law the bankruptcy of an individual entrepreneur i.e. the individual bankruptcy of a physical person; 4) work on passing a separate legislation in the field of the individual entrepreneurship, especially on passing and changing the laws which would regulate handicrafts (including old crafts and jobs of home industry), free professions as well as agricultural activity.

Mechanical power and motion are often transmitted by the gear-rotor-bearing system. When there are fitting clearances between the housing and outer ring in bearings, complex vibration responses will be generated, which makes the operating status difficult to identify. Therefore, for analyzing dynamic responses, a dynamic model of gear-rotor-bearing systems with bearing fitting clearances is proposed. In the model, the bearing system with fitting clearances and gear pair system are combined, and the coupling relationship is determined by the gyroscopic mo-tion of the shaft. The friction force and collision force caused by fitting clearances are also con-sidered, which are simulated by the Coulomb friction model and Hertz theory. The result shows that the meshing stiffness will also be excited by the bearing displacement. When there are fit-ting clearances on the bearing, the amplitude modulation of the bearing outer raceway to the system time-domain response is intensified, and the multiple harmonic frequency of bearings and gear pairs are generated on the spectrum, as well as defect frequencies of gear pairs. As the fitting clearance is increased, the higher multiple harmonic responses will be caused. Although the response amplitude of the gearbox is increased by raising the input speed, multiple harmon-ic responses are suppressed, which makes the system mainly vibrate at the fundamental fre-quency. Then, the dynamic model and the vibration analysis are experimentally verified.

Spatiotemporal variations of sediments transported along rivers play a crucial role in a wide spectrum of uses, such as navigation, recreation, habitats or hydropower production. The advancement in technology has made it possible to use various indirect techniques to study and evaluate the transport of suspended sediment in fluvial environments. To investigate large-scale phenomena, remote sensing is becoming a largely utilized approach, as it allows to combine spatially-distributed and local information. Dam-induced sediment reduction occurs in large and small rivers worldwide, having profound implications on the fluvial systems. However, the systematic change of suspended sediment concentration (SSC) and its dynamic processes are not well known, and generally investigated only at the very local scale using field information. In this work, SSC maps were created by combining satellite images with local monitoring SSC data, using the Zhijiang -Chenglingji reach of the Changjiang River (China) as a case study, and analyzing how SSC dynamics changed over the period 2017-2022 in correspondence of extreme events. Multiple relationships between measured SSC and bands reflectance were tested, showing the potential of the best-performing one (R2=0.43) in mapping SSC spatiotemporal variations over an extent of dozens of kilometres, eventually providing new insights into the SSC dynamics at hotspots of the river systems, such as confluence zones, barriers, and reservoirs.

Fractional delay-Doppler (DD) channel estimation in Orthogonal Time Frequency Space (OTFS) systems poses a significant challenge considering the severe effects of Inter-Path Interference (IPI). To this end, several algorithms have been extensively explored in literature for accurate low-complexity channel estimation in both integer and fractional DDs scenarios. In this work, we develop a variant of the state-of-the-art Delay-Doppler Inter-Path Interference Cancellation (DDIPIC) algorithm which progressively cancels the IPI as estimates are obtained. The key advantage of the proposed approach is that it requires only a final refinement procedure reducing the complexity of the algorithm. Specifically, the time difference in latency between the proposed approach and the DDIPIC algorithm is almost proportional to the square of the number of estimated paths. Numerical results show that the proposed algorithm outperforms the other channel estimation schemes achieving lower Normalized Mean Square Error (NMSE) and Bit Error Rate (BER).

We propose a novel moving median estimator specifically designed for online detection of threshold crossings in multi-channel signals, such as extracellular neural recordings. This estimator offers two key advantages: a reduced sensitivity to outliers and the elimination of memory requirements for storing arrival times. Furthermore, its design facilitates parallel implementation on FPGAs, making it ideal for real-time processing of multi-channel recordings.

Coffee is one of the most valuable agricultural products worldwide, and it is crucial to have efficient tools to obtain reliable information about production. This study aims to estimate coffee plantation production using segmentation and deep learning techniques in RGB images. Photographs of coffee plants were taken in Tabaconas, San Ignacio-Cajamarca, to create a dataset of crops during the harvest stage. The images were segmented to detect coffee fruits. A deep learning method was developed with YOLOv5 to detect the fruits and OpenCV to count them. The results showed that YOLOv5 achieved an accuracy of 97.25%, a recall of 95.77%, and an F1-Score of 96.37%, demonstrating high reliability in detecting coffee fruits. The average detection time per image was 17.9 seconds. The metrics were evaluated using a confusion matrix, highlighting the model's good performance. In conclusion, segmentation and deep learning techniques, along with counting algorithms developed with OpenCV, proved effective for estimating coffee production. This approach provides a valuable tool for farmers, improving crop management and facilitating decision-making in precision agriculture.

This study explores an optimal strategy for muscle force estimation by integrating simplified cost functions with detailed muscle models, demonstrating significant advantages over traditional complex methods. Using elbow flexion as a case study, we characterize the interplay between muscle models and cost functions, showing that accurate muscle force estimations can be achieved effectively without electromyography (EMG) data while also substantially reducing computational demands. Validated against established bio-mechanical data, our models confirm their accuracy in predicting muscle behavior. This approach has profound implications for enhancing rehabilitation protocols and athletic training, providing a more accessible and adaptable tool for muscle force analysis. It broadens the applicability of muscle force estimation in clinical and sports settings and paves the way for future innovations in bio-mechanical research.

Existing research concerning the effects of the sitting birth position during the second stage of labour on maternal and neonatal outcomes remains controversial, and there is a lack of studies to explore its effect on the childbirth experience. The objective of this study is to explore whether the sitting birth position would influence maternal and neonatal outcomes, as well as the childbirth experience reported by Chinese women using the Childbirth Experience Questionnaire (CEQ). A total of 296 women (including primiparous women and multiparous women) were enrolled in our study, and they were divided into the sitting position cohort (n=106) or the lithotomy position cohort(n=116). The study found that primiparous women in the sitting position cohort had a shorter duration of the second stage of labour, higher spontaneous vaginal birth rates, lower episiotomy rates and better childbirth experience (p<0.01). There was no significant difference for perineal lacerations, 2h-postpartum haemorrhage (P>0.05) between the two cohorts of primiparous women. After adjusting for confounding factors through multiple linear and logistic regression analyses, the results remained consistent with those reported above. No neonate in each cohort had Apgar scores at 1min and 5 min postpartum less than 7 or the Cord artery pH less than 7.00, regardless of parity. In addition, among multiparous women, there was no significant difference in any maternal and neonatal outcomes and women’s CEQ scores (p>0.05) between two cohorts. Based on the findings, we recommend that women could take the sitting birth position into account when giving birth for a positive childbirth experience, especially for primiparous women. The study could also serve as a reference for healthcare providers in the management of childbirth positions and the development of high-quality maternal care.

This study aims to uncover the chronology and production technologies of ancient bricks unearthed from various locations in Upper Assam, Northeast India. To achieve this goal, complementary spectroscopic techniques such as Fourier Transform Infrared (FTIR), X-ray Diffraction (XRD), Scanning Electron Microscope (SEM) coupled with Energy Dispersive Spectrometer (EDS), as well as Thermoluminescence (TL) and Optically Stimulated Luminescence (OSL/IRSL) dating have been applied. FTIR and XRD analyses revealed the presence of quartz, feldspar (microcline, orthoclase, albite), kaolinite, chlorite, cerussite, palygorskite, magnetite, hematite, and organic carbon. The mineralogical composition indicates two distinct groups with firing temperatures below 650°C and above ~800°C. These two groups could be the first indication of the presence of two civilizations or at least two different production technologies, involving different firing temperatures and kiln atmospheric conditions. Further, the SEM-EDS study suggests that both calcareous and non-calcareous clays were used in brick making, which have low and high refractory properties, respectively. The internal morphology of the samples shows the existence of micropores and microfractures, indicating the influence of higher temperature firing. Absolute dating techniques associate the two brick groups with different age ranges: a firing temperature above ~800°C indicates a superior technology corresponding to a production period between the 7th and 10th centuries CE. In contrast, a temperature below 650°C indicates a technologically less advanced group of people, with the age group dated between the 11th and 14th centuries CE.

This paper studies the pilot control strategy and workload of tilt-rotor aircraft dynamic conversion procedure between helicopter mode and fixed-wing mode. A nonlinear flight dynamics model of tilt-rotor aircraft with full flight modes is established. On this basis, a nonlinear optimal control model of dynamic conversion is constructed, considering factors such as conversion corridor limitations, pilot control, flight attitude, engine rated power, and wing stall effects. To assess pilot workload, an analytical method based on wavelet transform is proposed, which examines the mapping relationship between pilot control input's amplitude, constituent frequencies, and control tasks. By integrating the nonlinear optimal control model and the pilot workload evaluation method, the analysis of pilot control strategy and workload during the conversion procedure is conducted, leading to the identification of strategies to reduce pilot workload. The results indicate that incorporating the item of pilot workload in the performance index results in a notable reduction in the magnitude of collective stick inputs and longitudinal stick inputs. Moreover, it facilitates smoother adjustments in altitude and pitch attitude. Additionally, the conversion of the engine nacelle can be achieved at a lower and constant angular velocity. In summary, the conversion and reconversion procedures are estimated to have a low workload (Level 1 ~ Level 2), with relatively simple and easy manipulation for the pilot.

In the last decade, the drone market has grown rapidly for both civil and military purposes. Due to their versatility, drones demand is constantly increasing, with several industrial players joining the venture to transfer urban mobility to the air. This has exacerbated the problem of noise pollution, mainly due to the relatively lower altitude of these vehicles and to the proximity of their routes to extremely densely populated areas. In particular, both the aerodynamic and aeroacoustic optimization of the propulsive system and of its interaction with the airframe are key aspects of the design of aerial vehicles for the success or the failure of their mission. The industrial challenge involves finding the best performance in terms of loading, efficiency and weight, and, at the same time, the most silent configuration. For this reason, research has focused on an initial localization of the noise sources and, on further analysis, of the noise generation mechanism, focusing particularly on directivity and scattering. The aim of the present study is to review the noise source mechanisms and the state-of-the-art technologies available in literature for its suppression, focusing especially on the fluid-dynamic aspects of low Reynolds numbers of the propulsive system and on the interaction of the propulsive-system flow with the airframe.

The availability of multi-rotor UAVs with lifting capacities of several kilograms, allows for a new paradigm in atmospheric measurement techniques, i.e. the integration of research-grade sonic anemometers for airborne turbulence measurements. With their ability to hover and move very slowly, this approach yields an unrevealed flexibility compared to mast-based sonic anemometers, for a wide range of boundary layer investigations that require an accurate characterization of the turbulent flow. For an optimized sensor placement, potential disturbances by the propeller-induced flow (PIF), as well as the potential impact of the sensor weight and angular momentum on the flight performance in the case of a boom-mounted sensor, have to be considered.

Aerodynamic performance of a floating offshore wind turbine (FOWT) is significantly influenced by platform surging motions. Accurate prediction of the unsteady aerodynamic loads is imperative for determining the fatigue life, ultimate loads on key components such as FOWT rotor blades, gearbox and power converter. The current study examines the predictions of numerical codes by comparing with unsteady experimental results of a scaled floating wind turbine rotor. The influence of platform surge amplitude together with the tip speed ratio on the unsteady aerodynamic loading has been simulated through unsteady CFD. It is shown that the unsteady aerodynamic loads of FOWT are highly sensitive to the changes in frequency and amplitude of the platform motion. Also, the surging motion significantly influences the windmill operating state due to strong flow interaction between the rotating blades and generated blade-tip vortices. Almost in all frequencies and amplitudes, CFD, LR-BEM and LR-uBEM predictions of mean thrust shows a good correlation with experimental results.

First, we examine how spin is treated in special relativity and the necessity of introducing spin supplementary conditions (SSC) and how they are related to the choice of a center-of-mass of a spinning particle. Next, we discuss quantum electrodynamics and the Foldy-Wouthuysen transformation which we note is a position operator identical to the Pryce-Newton-Wigner position operator. The classical version of the operators are shown to be essential for the treatment of classical relativistic particles in general relativity, of special interest being the case of binary systems (black holes/neutron stars) which emit gravitational radiation.

Detecting the presence of explosives is important to protect human lives in military conflicts and peacetime. Gas-phase detection of explosives can make use of the change of material properties, which can be sensitive to environmental conditions such as temperature and humidity. This paper describes a remote-controlled automatic shutter method for environmental impact assessment of photoluminescence (PL) sensors under near-open conditions. Utilizing the remote-sensing method, we obtained environmental effects without being exposed to sensing vapor molecules, and explained how PL intensity was influenced by the temperature, humidity, and exposure time. We also developed a theoretical model including the effect of exciton diffusion for PL quenching, which worked well under limited molecular diffusions. Incomplete recovery of PL intensity, or degradation effect, was considered as an additional factor in the model.

Loop detectors are probably the widest-used technology for traffic state estimation. Previous research has shown that loop detector positions within the link significantly affect the estimation of the macroscopic fundamental diagram (MFD) of a given network. This paper examines the biases produced by the positioning of loop detectors on the MFD, using both analytical and simulation methods, as well as empirical data from UTD-19. We confirm earlier results that a uniform distribution of loop detector positions reduces the bias. For non-uniform distribution, we found that: (i) the subsets of the MFD by the loop detector position help estimate whether the loop detector MFD will have a bias; (ii) if the detectors in the network are positioned more downstream with a larger variation, the loop detector MFD is more likely to have a discrepancy in position subsets of the MFD; (iii) a lower ratio of link length to green signal time elevates the MFD bias as well. This research opens the possibility for the bias of MFD induced by the loop detector data to be corrected by only using itself.

This paper proposes a position control method for low cost EGR valve system in automotive application. Generally, position control system using in automotive application has many restrictions such as cost and space, the mechanical structure of actuator implies high friction and large difference between static friction and coulomb friction. This large friction difference occurs the vibrated position control result when the controller uses conventional linear controller such as P, PI. In this paper, low cost position control method which can apply under the condition of high difference friction mechanical system. Proposed method is verified by comparing conventional control result of experiments.

This paper proposes a new structure of the dual-rotor hybrid-excited axial-flux permanent magnet vernier machine (DR-HEAFPMVM) with the modular stator and the consequent-pole PM (CPM) rotor for low-speed, high torque density applications such as in-wheel electric vehicles. The tooth-wound non-overlapping armature windings and direct current (DC) excitation windings are respectively arranged in stator main-teeth and split-teeth to obtain the modulated and adjustable air-gap flux densities, which result in the high torque density and outstanding flux-weakening capability. First, the design consideration, operation principle, and the air-gap flux density distributions of the proposed machine are elaborated based on the air-gap permeance function. Then, the influence of the pole ratios (PRs) and the DC excitation currents on the main electromagnetic performances of the DR-HEAFPMVM, such as the flux-weakening capability, back-electromotive force (back-EMF), on-load electromagnetic torque, loss distribution, and efficiencies, is investigated using the 3-D finite-element method (FEM). Results verify the feasibility of the flux adjustment of the DC excitation windings equipped in the split-tooth, and the design with a pole ratio of 8/1 tends to have higher torque density, higher machine efficiency, and considerable flux-weakening capability compared with the other two PRs.

An inverse aeroacoustic problem for a helicopter rotor combined with aerodynamic constraint is proposed based on Ffowcs Williams and Hawkings equation in subsonic. The rotor noise includes thickness noise and loading noise when quadrupole noise is neglected. Thickness noise is related to geometry and motion conditions. Loading noise is related to the pressure on the wall. Therefore, the equation between pressure on the wall and far-field noise can be established, thus the pressure on the wall can be obtained by solving this equation. Since this equation is an ill-posed, the singular value decomposition combined with the regulation method is applied and the aerodynamic constraint is taken into account. The direct noise prediction is verify firstly and then the inverse problem is solved. The reconstruction pressure is compared to the input data. The result is in good agreement with the input value. At the same time, the influence of interference noise is also considered. Under low signal-to-noise ratio, the reconstruction result is also reasonable.

Recently, due to the miniaturization of electronic products, printed circuit boards (PCBs) have also become smaller. This trend has led to the need for high-precision electrical test equipment to check PCBs for disconnections and short circuits. The purpose of this study is to improve the position repeatability of the platform unit up to ±2.5 μm in a linear stage type test equipment. For this purpose, the causes of position errors of the platform unit are analyzed. The platform unit holding the PCB is driven by a single-axis linear ball screw drive system offset from its geometric center due to design constraints. The yaw rotation of the platform is found to have a dominant effect on position repeatability. To address this problem, the methods of adding balancing weights to the platform unit and adjusting the stiffness of LM Guides are proposed. This reduces the yaw rotation by moving the centers of mass and stiffness closer to the linear ball screw actuator. In the verification tests, the position repeatability was decreased to less than ±1.0 μm.

The pointing accuracy of large radio telescopes is affected by antenna structure errors, installation errors, servo errors, gravity, and varying environmental factors. It is hard to estimate the pointing error of radio telescopes under solar radiation via the traditional multi-parameter static pointing models, due to the time-varying characteristics of the dynamic pointing error. There is still no effective way to estimate the dynamic pointing error of large radio telescopes under solar radiation up to now. To estimate the dynamic pointing error of large radio telescopes under solar radiation, a new way based on digital twin(DT) is tried in this paper. The operational framework of the DT system of large radio telescopes is introduced first. Then, the Wuqing 70-m radio telescope(WRT70) is taken as the research object, the digital models of which are created. Finally, the reliability of the WRT70's DT system is verified and the dynamic pointing error of WRT70(staying at the position of the elevation angle of 90° and the azimuth angle of 180°) on two sunny days in different seasons is estimated by the created DT system. The research results show that the pointing error of large radio telescopes under solar radiation fluctuates greatly and the seasonal differences are very striking. The maximum elevation pointing error of WRT70 under solar radiation around the winter solstice is over 40 arcsec, which should never be ignored. The way is feasible to estimate the dynamic pointing error of large radio telescopes staying at a specific position under solar radiation via constructing a DT system in this paper, but it is not yet able to estimate effectively the dynamic pointing error of large radio telescopes being in action. It is expected to establish a dynamic pointing error model and calibrate the dynamic pointing errors of large radio telescopes being in action by DT technology in the future.

This article delves into the complex landscape of Bitcoin halving events, analyzing the historical context and implications of these pivotal occurrences within the cryptocurrency ecosystem and on its sustainability within the landscape of financial technologies. Through an examination of past halving events in 2012, 2016, and 2020, this study meticulously scrutinizes the conditions and prices of Bitcoin before and after each halving. By synthesizing data and trends from previous events, this article aims to uncover patterns and insights that illuminate the impact of Bitcoin halving on market dynamics and sustainability, movement of the price level, reached peaks and price troughs. Furthermore, this research presents a comparative analysis of past halving events, drawing parallels and distinctions to elucidate the evolving nature of Bitcoin's price movements and market sentiment surrounding these milestone events. Looking towards the future, this study estimates predictions and expectations for the upcoming Bitcoin halving. By unraveling the intricate relationship between Bitcoin halving events and market behavior, this article seeks to provide a comprehensive understanding of the broader implications for Bitcoin's trajectory and the evolving landscape of the cryptocurrency market and its sustainability.

Health care is currently showing a fall in heart failure (HF) prevalence and incidence particularly in developed countries, but with only a subset receiving appropriate therapy to protect the heart against maladaptive processes such as fibrosis and hypertrophy. Appropriate markers of advanced HF are still needed, which should help choosing the most suitable therapy and avoid major compliance problems. Speckle tracking echocardiography (STE) is a good choice, being a non-invasive imaging technique and is able to assess cardiac deformation in a variety of conditions. Several multicenter studies and meta-analyses have demonstrated its clinical applications and accuracy in early and late stages of HF, as well as its association with both left ventricular (LV) filling pressures and myocardial oxygen consumption, the two are important parameters in the evaluation of HF patients. Also, STE assists in assessing right ventricular free-wall longitudinal strain (RVFWLS) which is a solid predictor of right ventricle failure (RVF) following LV assist device (LVAD) implantation. Despite its limitations, STE plays an essential role in the clinical evaluation of patients with HF, early and advanced, in explaining symptoms and signs and also as an accurate prognosticator. The aim of this review is to examine the advantages of STE in early evaluation of myocardial dysfunction and their correlation with right heart catheterization (RHC) parameters, which should have a significant clinical relevance in the management of HF patients.

Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the performance of such devices exceeds 90\% in controlled environments, myoelectric devices still face challenges in robustness to variability of daily living conditions. Within this survey, the intrisic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where performance was 13% and 20% lower in realistic environments compared to controlled environments for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

A position sensing glove, called SmartScan, that creates a 3D virtual model of a real object is presented. The data from the glove is processed by a volume minimization algorithm to validate the position sensor data. This allows only data from the object’s surface to be retained. The data validation algorithm allows the user to progressively improve an image by repeatedly moving their hand over the object. In addition, the user can choose their own balance between feature resolution and invalid data rejection. The SmartScan glove is tested on a foot model and is shown to be robust against motion artifacts, and has a mean accuracy of 2.9 mm (compared to a 3D model generated from optical imaging) without calibration.

Coaxial rotors can be found in multirotor vehicles for the added thrust compared to independent rotors while keeping similar area footprints but, performance losses should be considered. This experimental study analyzes the effects of varying motor throttle and propeller pitch values in motor-propeller systems with two to four coaxial rotors. The results show that in a two-rotor coaxial system, to lessen the adverse effects of a front rotor’s backwash and to operate at the maximum performance, only the back motor should be operated initially up to 75% duty cycle before using the front motor up to its 75% duty cycle. Additional thrust requirements should be generated from the back rotor and then from the front rotor up to their maximum duty cycles. In two, three, and four-rotor coaxial setups, total thrust output generated is 1.6, 2.1, and 2.5 times the thrust output at system thrust performance of 86%, 76%, and 66%, respectively of that of an isolated rotor. In a four-rotor coaxial setup, maximum system performance is achieved when the propeller pitch values are gradually increased from the first to the last rotor. The gradual increments in propeller pitch values also result in more uniform thrust sharing among rotors.

The use of sensors in monitoring of lateral accelerations in delivery van transport focuses on measuring lateral accelerations on routes with roundabouts and curves to increase road safety. Using microelectromechanical system (MEMS) sensors, it measures the lateral accelerations acting on the vehicle and the load being transported during the test drives to study vehicle dynamics for cargo securing. Using an accelerometer and position tracking, accelerations can be detected when traversing curves and roundabouts at selected locations on the vehicle. The level of accelerations on the roof of the vehicle was found to be like those occurring on a lashed load with limited movements, where regression analysis can be used to determine the relationship between lateral accelerations at different sensor positions. If we compare the mean values of the lateral accelerations of the individual events between the sensors, the sensor on the side of the vehicle body at the height of the sensor on the load had approximately 5% lower mean values than the sensor on the roof. The sensor on the load measured approximately 5% higher mean values than the sensor on the roof. Hence, the mean lateral accelerations of the individual events for the sensor on the load are 10% higher than for the sensor at the same height on the vehicle body. From the above testing, we can say that the values of the mean lateral accelerations of the van from the sensor on the roof of the vehicle, are closer to the values of the accelerations of the sensor on the load than to the values of the sensor on the body of the vehicle at the same height.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

The present investigation focuses on the impact of jet nozzle orientation on mixing time in a cylindrical tank. The aim is to identify nozzle positions that improve mixing performance and to elucidate the governing parameters that influence it. A water tank was employed for the experiment. The vertical inclination angle (α) and the horizontal inclination angle (β) of the jet nozzle determined the nozzle positions. Mixing time was determined using an inert tracer and spectrophotometry measurements. The findings show that the mixing time is significantly influenced by the position of the jet nozzle position. The accuracy of existing jet turbulence and the circulation models for the prediction of mixing time was evaluated for the different nozzle positions. Our results indicate that both models provide accurate predictions for the conventional centrally aligned (β = 0°), upward-pointing jet nozzle positions only (α > 0). For the other nozzle positions where β > 0° and at varying α, the data follow the same trends as the jet turbulence and circulation models, however, the proportionality constants vary. Shorter mixing times can be attributed principally to longer jet path lengths and therefore higher fluid entrainment and circulation, as well as higher dissipation rates per jet length squared. However, it is suspected that the 3-dimensional nature of the flow pattern generated in the tank also plays a non-negligible role since mixing is hindered when the nozzle points more towards the tank wall.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

In this paper, COMSOL Multiphysics software was used to simulate and analyze the process of cleaning oily sludge by high-speed stirring rotor. The physical phenomena such as velocity field, pressure field, heat dissipation and cavitation generated by the rotor during the process of the oily sludge cleaning were analyzed. The results showed that in the process of cleaning the oily sludge by high-speed stirring rotor, the shear force, heat dissipation and pressure increased with the enhance in the rotor speed. Cavitation occurs in the impeller area of the rotor because of the change of pressure, so the cavitation bubbles were formed. Shear force, heat dissipation and cavitation contributed to the separation of oil phase in oily sludge. Meanwhile, the simulation results were verified by experiments.

Permanent magnet synchronous motor (PMSM) AC servo system shows the characteristics of uncertainty, time-varying and non-linearity, which makes it difficult for traditional PID control to achieve ideal control effect. Fuzzy control has strong adaptability to the problems of parameter variation, non-linearity and model inaccuracy of the controlled object. Vector control strategy is used to study its control principle and the realization of SVPWM. Because the motor object has certain fractional order characteristics, the fuzzy parameter self-tuning PIλ control is chosen as the position regulator of servo motor. It combines the accuracy of fractional order PI control and the adaptability of fuzzy control. A simulation model of PMSM three-closed-loop system is built in MATLAB/Simulink environment. The results show that the control method is effective and can satisfy the trajectory tracking of servo control.

Detection of human movement requires lightweight, flexible systems to detect mechanical parameters (like strain and pressure) not interfering with user activity, and that he/she can wear comfortably. In this work we address such multifaceted challenge with the development of smart garments for lower limb motion detection, like a textile kneepad and anklet in which soft sensors and readout electronics are embedded for detecting movement of the specific joint. Stretchable capacitive sensors with a three-electrode configuration are built combining conductive textiles and elastomeric layers, and distributed at knee and ankle. They show an excellent behavior in the ~30% strain range, hence the correlation between their responses and the optically tracked Euler angles is allowed for basic lower limb movements. Bending during knee flexion/extension is detected, and it is discriminated from any external contact by implementing in real time a low computational algorithm. The smart anklet is designed to address joint motion detection in and off the sagittal plane. In this work, ankle dorsi/plantar flexion, adduction/abduction, and rotation are retrieved. Both smart garments show a high accuracy in movement detection, with a RMSE less than 4° in the worst case.

The Simultaneous Localization And Mapping (SLAM) scanner is an easy and portable Light Detection And Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO Scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data are used: the Total Station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons are tested: cloud to cloud, cloud to mesh, mesh to mesh, and edge detection board assessment. However, the results of the assessments confirm that the accuracy of indoor SLAM scanner measurements (5 mm) is greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud to cloud provides the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it is confirmed that SLAM scanners are effective tools for data acquisition.

In this paper, a 3 DOF gyrscope position control have been designed and controlled using optimal control theory. An input torque has been given to the first axis and the angular position of the second axis have been analyzed while the third axis are kept free from rotation. The system mathematical model is controllable and observable. Linear Quadratic Integral (LQI) and Linear Quadratic State Feedback Regulator (LQRY) controllers have been used to improve the performance of the system. Comparison of the system with the proposed controllers for tracking a desired step and random angular position have been done using Matlab/Simulink Toolbox and a promising results has been analyzed.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Pyrops candelaria is one of the common pests of fruit trees, but the research on the pathogenic microorganisms it may carry is very limited. Therefore, it is essential to reveal the pathogenic microbes it carries and their potential hazards. This study found a new virus from the transcriptome of P. candelaria, which was first reported in P. candelaria and named as PyCaV (Pyrops candelaria associated virus). RACE and bioinformatics assay revealed that the full-length of PyCaV is 10855 bp with the polyA tail, containing a single open reading frame (ORF) encoding a polyprotein consisting of 3171 amino acid (aa). The virus has a typical Iflavirus structure, including two rhv domains, a RNA helicase domain (HEL), a 3C cysteine protease domain (Pro), and a RNA-dependent RNA polymerase domain (RdRp). Further phylogenetic analysis revealed that this virus belongs to family Iflaviridae and sequence alignements analysis suggedted PyCaV is new member in unassigned genus of family Iflaviridae. Further in-depth analysis of virus-infection-part showed that PyCaV is distributing in the whole longan lanternfly, including its head, chest, and abdomen, but more PyCaV was identified in the chest. The distribution of PyCaV in different parts of P. candelaria was further explored, which showed that more PyCaV were detected in its piercing-sucking mouthparts and chest viscera. Statistical analysis showed that the PyCaV infection was affected by time and locations.

Background: The bacterium Capnocytophaga spp. is a part of the oral microbiome of dogs, which has zoonotic potential. This study aimed to assess factors that influence the prevalence of Capnocytophaga cynodegmi and Capnocytophaga canimorsus in Wrocaw’s dog population. Methods: Molecular detection (PCR) involved 105 swabs, examining parameters such as oral cavity condition, underlying health issues, recent antibiotics, age, medications, material collection, and dog chews. Results: The study highlighted significant associations between the presence of Capnocytophaga cynodegmi and oral cavity condition, material collection method, and use of dog’s chews. Conclusions: Correlations obtained in this study between the oral health of Wroclaw's dog population and the presence of Capnocytophaga cynodegmi suggest its significance in the oral microbiome of dogs. Limitations, including the small sample size, highlight the need for more research to comprehensively understand additional factors that affect the prevalence of Capnocytophaga spp. in dogs

As the world population grows, fulfilling the need for biomaterials in the healthcare system becomes increasingly important. Biomaterials, which include metals, polymers, ceramics, and composites, play an important role in the creation of medical devices that improve people's lives. The European Society for Biomaterials described biomaterials as non-biological materials used in medical devices to interact with biological systems. This definition has developed to encompass materials that treat, assess, or replace tissues or bodily functions. Biocompatibility, as defined by the FDA, is a key feature of biomaterials, as is the requirement to fulfil design requirements such as mechanical performance, geometry, and electrical control. Over the last decade, advances in regenerative medicine and tissue engineering have boosted biomaterials' usage in medical applications dramatically. Despite its promise, scientists, engineers, and physicians continue to face significant challenges due to the complexity of biomaterials. Biomaterials have various benefits, including biodegradability, non-toxicity, and biocompatibility, making them extremely useful for replacing, sustaining, or improving biological activities in damaged tissues. They are used in a wide range of applications, including tissue engineering implants, human tissue healing and regeneration, medication delivery systems, and biosensors. This study gives insights into the fundamental characteristics, case studies, and ethical issues associated with the usage and use of biomaterials, further examining their potential and significance in healthcare and medicine.