Search

Although digitalization is a prevalent strategy in reward programs, a minimal amount is known regarding the relationship between reward app usage and reward redemption behaviors. The enhanced availability and accessibility of information via mobile app can alter customers' reward-redemption patterns and engagement level, highlighting the need for firms to adjust their reward-based service strategies. Using unique datasets that reflect individual consumers' transactional and reward redemption behaviors, we conducted a series of analyses to capture the dynamics in the latent engagement state transitions of customers according to the type of reward program used. Results show that although reward app usage, on average, induces a more active engagement state, it polarizes the volatility of engagement state transitions. Furthermore, we find that greater volatility of engagement state transitions is significantly associated with a higher likelihood of churn. Finally, a follow-up survey and panel vector autoregression analyses are conducted to uncover the mechanisms that underlie the relationship. The results provide insights into how retailers can strategically design their reward programs in the emerging mobile-based omnichannel environment. [ABSTRACT FROM AUTHOR]

Space vector pulse width modulation (SVPWM) is widely used in three-phase inverters. As the performance requirements of inverters increase, there is a demand to suppress common-mode voltages (CMVs) generated by SVPWM. In order to suppress the CMVs, it is necessary to mathematically analyze the CMVs. By using a mathematical analysis method based on double Fourier series, general expressions of CMV harmonic amplitudes and spectra are obtained for seven-segment SVPWM and five-segment SVPWM. Comparative analyses on the CMV general expressions are performed for the two SVPWMs, and the CMV harmonics characteristics for the two SVPWMs are summarized. Simulations are carried out in an inverter-driven permanent magnet motor system, and simulation results are in good agreement with calculation ones, which verifies the correctness and validity of the mathematical analysis. Based on these analyses, a more in-depth research can be conducted on the CMV suppression. [ABSTRACT FROM AUTHOR]

Background: To analyze the difference and agreement between measurements obtained by a new fully automatic optical biometer, the SW-9000 μm Plus, based on optical low-coherence reflectometry (OLCR) and a commonly used optical biometer (Pentacam AXL) based on Scheimpflug imaging with partial coherence interferometry (PCI). Methods: The central corneal thickness (CCT), anterior chamber depth (ACD, from epithelium to anterior lens surface), lens thickness (LT), mean keratometry (Km), corneal astigmatism, corneal diameter (CD), pupil diameter (PD), and axial length (AL) of 74 eyes (from 74 healthy subjects) were measured using the SW-9000 μm Plus and the Pentacam AXL to determine the agreement. Double angle plots were used for astigmatism vector analysis. Bland–Altman and 95% limits of agreement (LoA) were calculated. Results: Statistically significant differences were detected for all parameters but J0 vector. The Bland–Altman analysis of AL, CCT, ACD, Km, CD, J0 and J45 indicated a high level of agreement between the two devices. Among AL, CCT, ACD, Km, J0, J45, CD, and PD, the 95% LoA ranged from -0.07 to 0.05 mm, -9.67 to 7.34 mm, -0.11 to 0.04 mm, -0.25 to 0.50 D, -0.22 to 0.20 D, -0.15 to 0.20 D, -0.23 to 0.35 mm and 1.55 to 3.77 mm, respectively. Conclusions: The measurements of AL, CCT, ACD, Km, corneal astigmatism, and CD showed a narrow LoA and may be used interchangeably in healthy subjects between the new OLCR optical biometer and the Scheimpflug/PCI biometer; however, a poor agreement was noted for PD values. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodsResultsConclusionsTo compare the impact of 8 mm versus 9 mm optical zone (OZ) diameters on corneal astigmatism correction and to evaluate the impact of incision depth percentage after femtosecond laser corneal relaxing incisions (CRIs) combined with cataract surgery.Thirty-nine eyes from 39 patients were enrolled and randomly assigned to two groups based on the OZ diameter: 8 mm and 9 mm. Corneal astigmatism and CRI depth were measured using Pentacam and anterior segment optical coherence tomography. Vector analysis was performed using Alpins method.In the 8 mm group, postoperative corneal astigmatism was reduced by 0.77 ± 0.61 diopters (D), significantly greater than the reduction of 0.42 ± 0.40 D in the 9 mm group (p < 0.05). The 8 mm group exhibited greater surgically induced astigmatism (SIA), net corneal changes, and a smaller angle of error (AE) (p < 0.05). At 3 months postsurgery, the correction index (CI) values were 0.84 ± 0.39 for the 8 mm group, exceeding the 0.52 ± 0.32 observed in the 9 mm group (p < 0.05). The mean incision depth percentage was significantly higher in the 9 mm group (79.30 ± 10.76%) compared to the 8 mm group (72.58 ± 8.73%) (p < 0.05). In the 8 mm group, CI values closer to 1.00 were associated with lower percentages of incision depth.CRIs with an 8 mm OZ diameter demonstrated superior efficacy in correcting corneal astigmatism with shallower incision depths compared to those with a 9 mm diameter. [ABSTRACT FROM AUTHOR]

The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector's hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively. [ABSTRACT FROM AUTHOR]

In this article, we develop a methodological approach for organizational research regarding the construction of multidimensional and relational similarity measures by using the vector space model in natural language processing (NLP). Our vector space approach draws on the well-established premise in organizational research that texts provide a window into social reality and allow measuring theory-based constructs (e.g., organizations' self-representations). Using a vector space approach allows capturing the multidimensionality of these theory-based constructs and computing relational similarities between organizational entities (e.g., organizations, their members, and subunits) in social spaces and with their environments, such as the organization itself, industries, or countries. Thus, our methodological approach contributes to the recent trend in organizational research to use the potential inherent in big (textual) data by using NLP. In an example, we provide guidance for organizational scholars by illustrating how they can ensure validity when applying our methodological contribution in concrete research practice. [ABSTRACT FROM AUTHOR]

In long‐range missions based on angle‐of‐arrival positioning, the angle measurement error of unmanned aerial vehicles is a major source of error. Therefore, reducing the unmanned aerial vehicle angle measurement error is crucial to achieve accurate remote positioning. In this paper, we propose a space vector–based method to correct the space vector of the target for the unmanned aerial vehicles when there are fewer than three available reference points, which in turn corrects the angular value of the target relative to the unmanned aerial vehicles. Simulation results show that when the distance between the reference point and the unmanned aerial vehicles is smaller than the distance between the target and the unmanned aerial vehicles, the azimuth measurement error can be reduced to 55% of the original error for the case of a single reference point, while the pitch angle measurement error remains almost unchanged. In the case of more than two reference points, the azimuth measurement error can be reduced to 1e5 and the pitch angle measurement error can be reduced to 30% of the original error. This method can be adapted to the rapid positioning task for high‐speed and high‐mobility targets without iteration, low computation, good correction effect, and the need of prior known data set reference. [ABSTRACT FROM AUTHOR]

Inverter-based systems encounter significant challenges in mitigating common-mode voltage (CMV) and minimizing inverter losses. Despite various space vector pulse-width modulation (SVPWM) techniques proposed to address these issues, a comprehensive comparative analysis has been lacking. This paper addresses this gap through an experimental and simulation-based evaluation of nine SVPWM techniques. A new discontinuous SVPWM technique, DSVPWM-K4, is introduced, which involves reversing the use of the two zero vectors in DSVPWM-K3. DSVPWM-K3 delivers superior performance in terms of CMV reduction, total harmonic distortion (THD), and inverter losses across all modulation indices (MI = 1, 0.75, 0.5, and 0.25), making it the most effective overall. Although DSVPWM-K4 is a novel approach, it ranks second in effectiveness. The RSPWM technique achieves the lowest CMV with a zero peak-to-peak value but is most effective at lower modulation indices (0.25 and 0.5) due to higher harmonic distortion at higher modulation indices. AZSPWM performs optimally at higher modulation indices, providing a 66.66% reduction in CMV compared to continuous SVPWM and significantly lower THD compared to RSPWM. In contrast, NSPWM exhibits nearly double the THD compared to continuous SVPWM. [ABSTRACT FROM AUTHOR]

Accurate understanding of urban land use change information is of great significance for urban planning, urban monitoring, and disaster assessment. The use of Very-High-Resolution (VHR) remote sensing images for change detection on urban land features has gradually become mainstream. However, most existing transfer learning-based change detection models compute multiple deep image features, leading to feature redundancy. Therefore, we propose a Transfer Learning Change Detection Model Based on Change Feature Selection (TL-FS). The proposed method involves using a pretrained transfer learning model framework to compute deep features from multitemporal remote sensing images. A change feature selection algorithm is then designed to filter relevant change information. Subsequently, these change features are combined into a vector. The Change Vector Analysis (CVA) is employed to calculate the magnitude of change in the vector. Finally, the Fuzzy C-Means (FCM) classification is utilized to obtain binary change detection results. In this study, we selected four VHR optical image datasets from Beijing-2 for the experiment. Compared with the Change Vector Analysis and Spectral Gradient Difference, the TL-FS method had maximum increases of 26.41% in the F1-score, 38.04% in precision, 29.88% in recall, and 26.15% in the overall accuracy. The results of the ablation experiments also indicate that TL-FS could provide clearer texture and shape detections for dual-temporal VHR image changes. It can effectively detect complex features in urban scenes. [ABSTRACT FROM AUTHOR]

Over the past few decades, the oceans have stored the majority of the excess heat in the climate system resulting from anthropogenic emissions. An accurate, long-term sea surface temperature (SST) dataset is essential for monitoring and researching the changes to the global oceans. A variety of SST datasets have been produced by various institutes over the years, and here, we present a new SST data record produced originally within the Copernicus Marine Environment Monitoring Service (which is therefore named CMEMS v2.0) and assess: (1) its accuracy compared to independent observations; (2) how it compares with the previous version (named CMEMS v1.2); and (3) its performance during two major volcanic eruptions. By comparing both versions of the CMEMS datasets using independent in situ observations, we show that both datasets are within the target accuracy of 0.1 K, but that CMEMS v2.0 is closer to the ground truth. The uncertainty fields generated by the two analyses were also compared, and CMEMS v2.0 was found to provide a more accurate estimate of its own uncertainties. Frequency and vector analysis of the SST fields determined that CMEMS v2.0 feature resolution and horizontal gradients were also superior, indicating that it resolved oceanic features with greater clarity. The behavior of the two analyses during two volcanic eruption events (Mt. Pinatubo and El Chichón) was examined. A comparison with the HadSST4 gridded in situ dataset suggested a cool bias in the CMEMS v2.0 dataset versus the v1.2 dataset following the Pinatubo eruption, although a comparison with sparser buoy-only observations yielded less clear results. No clear impact of the El Chichón eruption (which was a smaller event than Mt. Pinatubo) on CMEMS v2.0 was found. Overall, with the exception of a few specific and extreme events early in the time series, CMEMS v2.0 possesses high accuracy, resolution, and stability and is recommended to users. [ABSTRACT FROM AUTHOR]

Introduction: Athletic performance is greatly impacted by hydration status. The combination of several techniques is recommended to accurately measure water losses and gains. Aim: The aim of this study is to assess the validity of bioelectrical impedance vector analysis (BIVA) as a tool for measuring hydration status in endurance and strength athletes. Methods: A total of 148 athletes were evaluated on one experimental day, pre- and post-training. Urine samples were collected and analyzed for color and specific gravity. Body weight changes were measured, sweat rate was calculated, and BIVA was performed. Reference ellipses were plotted using data of 200 healthy non-athletic individuals. Results: A moderate significant agreement was noted between raw bioelectrical values and urine specific gravity (USG) (p > 0.05). The sensitivity of classic BIVA in detecting minor changes in hydration status is confirmed both graphically and statistically. R/h and Z statistically significantly decreased post-training. Male athletes exhibited a specific BIA vector distribution compared to the reference population and were slightly more hydrated than female athletes. Conclusions: BIVA validation may be an essential step to allow its use among university students to assess dehydration in a non-invasive, practical, and inexpensive way. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study was to evaluate alterations in corneal astigmatism, axial anterior corneal curvature, anterior chamber depth, and central corneal thickness (CCT) two months after the unilateral recession of lateral rectus muscle in children. Methods: This prospective study included 37 children with intermittent exotropia who would undergo unilateral lateral rectus muscle recession. All measurements were performed using Pentacam®. Comparisons were made between the operated and fellow unoperated eyes, pre- and post-operatively. The assessment was made for changes in the radius of axial curvature on major meridians at 3 and 3.5 mm from the optical corneal center in the mid-peripheral zone. Astigmatism changes of the anterior and posterior corneal surface were calculated using vector analysis software (astigMATIC®). The interaction between age or CCT and postoperative changes in anterior and posterior surface corneal astigmatism were examined with ANOVA model. Results: In the intervention group, changes in anterior and posterior corneal surface astigmatism were statistically significant, with a mean increase of 0.56Dx90 and 0.08Dx87, respectively. In the mid-peripheral corneal zone, an increase was observed in the radius of anterior corneal axial curvature, more evident temporal 3 and 3.5 mm from the corneal center on the horizontal meridian, with corresponding decrease superiorly and inferiorly at 3 and 3.5 mm from the corneal center on the vertical meridian. Discussion: The changes in total astigmatism of the operated eyes are mainly attributed to the anterior corneal surface. These changes are associated with flattening in the 180 meridian of the cornea, leading to a shift to “with-the-rule” astigmatism. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this research is to explore the applicability of machine learning and fully homomorphic encryption (FHE) in the private pathological assessment, with a focus on the inference phase of support vector machines (SVM) for the classification of confidential medical data. Methods: A framework is introduced that utilizes the Cheon-Kim-Kim-Song (CKKS) FHE scheme, facilitating the execution of SVM inference on encrypted datasets. This framework ensures the privacy of patient data and negates the necessity of decryption during the analytical process. Additionally, an efficient feature extraction technique is presented for the transformation of medical imagery into vectorial representations. Results: The system's evaluation across various datasets substantiates its practicality and efficacy. The proposed method delivers classification accuracy and performance on par with traditional, non-encrypted SVM inference, while upholding a 128-bit security level against established cryptographic attacks targeting the CKKS scheme. The secure inference process is executed within a temporal span of mere seconds. Conclusion: The findings of this study underscore the viability of FHE in enhancing the security and efficiency of bioinformatics analyses, potentially benefiting fields such as cardiology, oncology, and medical imagery. The implications of this research are significant for the future of privacy-preserving machine learning, promoting progress in diagnostic procedures, tailored medical treatments, and clinical investigations. [ABSTRACT FROM AUTHOR]

Seismic imaging of complicated underground structures with severe surface undulation (i.e., double complex areas) is challenging owing to the difficulty of collecting the very weak reflected signal. Enhancing the weak signal is difficult even with state-of-the-art multi-domain and multidimensional prestack denoising techniques. This paper presents a time–space dip analysis of offset vector tile (OVT) domain data based on the τ-p transform. The proposed N-th root slant stack method enhances the signal in a three-dimensional τ-p domain by establishing a zero-offset time-dip seismic attribute trace and calculating the coherence values of a given data sub-volume (i.e., inline, crossline, time), which are then used to recalculate the data. After sorting, the new data provide a solid foundation for obtaining the optimal N value of the N-th root slant stack, which is used to enhance a weak signal. The proposed method was applied to denoising low signal-to-noise ratio (SNR) data from Western China. The optimal N value was determined for improving the SNR in deep strata, and the weak seismic signal was enhanced. The results showed that the proposed method effectively suppressed noise in low-SNR data. [ABSTRACT FROM AUTHOR]

In order to solve the problem of real-time and accurate recognition of coal gangue in the intelligent separation system of coal gangue, an online visual recognition algorithm of coal gangue based on BLOB analysis and machine learning is proposed. It filters the easily recognized gangue or coal by triple filter model with small calculation, which only discriminating the suspected gangue image extremely difficult to recognize. The remaining small amount of suspected coal gangue image is distinguished by calculating the local characteristic parameters and inputting them into the SVM classification model. The algorithm has been applied to the intelligent sorting system of coal gangue and verified by experiments. The test results show that it improves the recognition rate of coal gangue and ensures the real-time detection. [ABSTRACT FROM AUTHOR]

Purpose: Alpine ski racing is a complex sport where no single factor can exclusively account for performance. We assessed body composition, using bioelectrical impedance vector analysis (BIVA), and our purpose was to study its influence on the strength and power profile of young alpine skiers. Methods: Anthropometric measurements and advanced BIVA parameters were recorded on eighteen alpine ski racers (6 females/12 males; 17.0 ± 1.3 years; 172.2 ± 9.3 cm; 68.5 ± 9.8 kg). Dynamic force and power were assessed using countermovement jumps (CMJ), while maximal isometric strength was evaluated for hip flexion-extension and abduction-adduction movements. Stepwise regression models examined the relationship between BIVA-derived parameters and strength/power variables. Results: Body cellular mass (BCM) positively related to jump height (p =.021, R2 = 74%), jump momentum (p <.001, R2 = 89%), reactive strength index modified (p =.026, R2 = 75%) and peak concentric power (p <.001, R2 = 82%), while intracellular water (ICW) related to peak eccentric (p <.001, R2 = 76%) and concentric force (p <.001, R2 = 77%) as well as to concentric rate of force development together with the phase angle (PhA) (p =.008, R2 = 79% and R2 = 0.015). Regarding isometric assessment, ICW was a significant predictor for all four movement directions, and PhA contributed to hip adduction strength. Conclusions: Body composition, particularly BCM and ICW, significantly predict force- and power-related factors in young alpine skiers. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The three-level (3-L) neutral-point-clamped (NPC) converter has found widespread applications in various fields, due to its simple structure, low failure rate and high efficiency. However, the 3-L NPC converter suffers the issue of unbalanced neutral-point (NP) voltage due to the divided two dc-side capacitors. The large NP voltage ripple and offset Based on the detailed analysis of the effects of vectors and clamping patterns on the NP voltage, a discontinuous optimized modulation method for NPC grid-connected inverter with both low switching loss and ability to balance NP voltage is presented. By measuring NP voltage and utilizing the property that superfluous clamping patterns have opposing effects on the NP voltage, the modulation method adjusts the NP voltage with the injection of various zero sequence voltages (ZSV) into the original sinusoidal waves. The results of this study demonstrate that the CB-DPWM can control the NP voltage balanced and decrease switching loss compared to conventional DPWM and SVPWM strategies. Moreover, the CB-DPWM strategy does not need to identify sectors and subsectors or calculate dwelling times. This modulation strategy effectively suppresses low-frequency fluctuations and deviations of the NP voltage with the injection of a simple ZSV, and improves the power quality of the output voltages and currents. Compared to the traditional SVPWM scheme, the suggested CB-DPWM has a 27 % lower overall IGBT loss. Moreover, this method is implemented through carrier waves, which is simpler than modulation schemes based on space vector, greatly shortens the program running time, and is conducive to engineering implementation. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the three-year visual, refractive outcomes and adverse events of the Eyecryl toric phakic IOL (pIOL) for moderate-to-high myopic astigmatism. Methods: This retrospective study included eligible patients who underwent refractive surgery in one or both eyes with Eyecryl toric pIOL for myopic astigmatism. The efficacy, safety, predictability, rotational stability, vector analysis, and adverse events were evaluated in patients with spherical refraction from − 4.50 to -17.00 diopters (D) and cylindrical refraction from − 0.75 to -5.50 D. Results: Fifty-two eyes of 28 patients were included in the study. The mean efficacy and safety index were 1.12 ± 0.35 and 1.38 ± 0.42, respectively. The mean manifest refraction spherical equivalent was − 10.06 ± 2.69 D and − 0.64 ± 0.61 D preoperatively and postoperatively at 36 months, respectively. The mean manifest astigmatism was − 2.06 ± 1.16 D and − 0.44 ± 0.48 D preoperatively and 36 months postoperative, respectively. During the final examination, 70% of the eyes showed an increase in CDVA of one or more lines compared to their preoperative state. There was a cumulative endothelial cell loss of 3.1% at 36 months postoperatively. One eye developed visually significant anterior subcapsular opacity, whereas another eye experienced pIOL opacification. Conclusion: The Eyecryl toric pIOL demonstrated satisfactory visual acuity and refractive outcomes, as assessed by efficacy, safety and stability over a three-year period. [ABSTRACT FROM AUTHOR]

Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain–computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7 % . [ABSTRACT FROM AUTHOR]

Trajectory-following control is the basis for the practical application of an articulated virtual rail train transportation system. In this paper, a planar nonlinear dynamics model of an articulated vehicle is derived using the Euler–Lagrange method. A trajectory-following control strategy based on the first following point is proposed, and a feedback linearization control algorithm is designed based on the vehicle dynamics model to achieve the trajectory following of the rear vehicle. Based on the target trajectory formed by the first following point and measured by virtual sensors, a vector analysis method grounded in geometric relationships is proposed to solve in real time for the desired position, velocity, and acceleration of the vehicle. Finally, a MATLAB/SIMPACK dynamics virtual prototype is established to test the vehicle's trajectory-following effectiveness and dynamics performance under lane change and circular curve routes. The results indicate that the control algorithm can achieve trajectory following while maintaining good vehicle dynamics performance. It is robust to variations in vehicle mass, vehicle speed, tire cornering stiffness, and road friction coefficient. [ABSTRACT FROM AUTHOR]

Introduction: Rhizosphere effects (REs) have recently been identified as important regulators of root and microbial nutrient acquisition and are positively involved in nutrient cycling of belowground carbon (C), nitrogen (N), and phosphorus (P). Nutrient conditions of the fine roots and soil N are likely to influence REs. Still, it is unclear how REs of soil nutrients themselves variably impact the supply of nutrients to plants in terms of the responses to soil N due to succession. Methods: In this study, we applied both fine roots and extracellular enzymes for vector analysis and stoichiometry of N:P to explore the metabolic limitations of roots and rhizospheric soil microbes and their relationships with REs across five levels of soil N (0, 5, 10, 15, and 20 kg N m-2 year-1) along successional age classes of 42, 55, and 65 years in a Pinus tabuliformis forest. Results: Overall, the metabolism of root and rhizospheric soil microbes was mediated by soil N. N limitation of roots initially decreased before increasing, whereas that of microbes demonstrated opposite trends to the N levels owing to competition for inorganic N between them by REs of NO3--N. However, N limitations of both roots and microbes were alleviated in young stands and increased with succession after the application of N. In addition, root N limitations were manipulated by REs of three different soil N-related indicators, i.e., total N, NH4 +-N, and NO3 --N. Rhizospheric soil microbial N limitation was almost unaffected by REs due to their strong homeostasis but was an important driver in the regulation of root N limitation. Discussion: Our results indicated that successional age was the most critical driver that directly and indirectly affected root N metabolism. However, the level of N application had a slight effect on root N limitation. Microbial N limitation and variations in the REs of N indicators regulated root N limitation in the rhizosphere. As a result, roots utilized REs to sequester N to alleviate N limitations. These findings contribute to novel mechanistic perspectives on the sustainability of N nutrition by regulating N cycling in a system of plant-soil-microbes in the rhizosphere to adapt to global N deposition or the heterogeneous distribution of bioavailable soil N with succession. [ABSTRACT FROM AUTHOR]

This study aimed to establish a virtual cross cylinder method to calculate the total corneal astigmatism by combining the anterior and posterior corneal astigmatism on the secondary principal plane of the cornea based on Gaussian optics. The meridian with the least refractive power, namely, the flattest meridian of the virtual cross cylinder of a ± 0.5 × C diopter, is set as the reference meridian, and the power (F) at an angle of φ between an arbitrary meridian and the reference meridian is defined as F(φ) = − 0.5 × C × cos2φ. The magnitude and axis of the total corneal astigmatism were calculated by applying trigonometric functions and the atan2 function based on the combination of the virtual cross cylinders of the anterior corneal astigmatism and the posterior corneal astigmatism. To verify the performance of the virtual cross cylinder method, a verification experiment with two Jackson cross cylinders and a lensmeter was performed, and the measured and calculated values were compared. The limit of the natural domain of the arctangent function is circumvented by using the atan2 function. The magnitude and axis of the total corneal astigmatism are determined through generalized mathematical expressions. The verification experiment results showed good agreement between the measured and calculated values. Compared to the vector analysis method, the virtual cross cylinder method is mathematically sound and straightforward. A novel technique for calculating total corneal astigmatism, the virtual cross cylinder method, was developed and verified. [ABSTRACT FROM AUTHOR]

Driven by the scientific objective of geophysical field detection and natural hazard monitoring from space, China launched an electromagnetic satellite, which is known as the China Seismo-Electromagnetic Satellite (CSES-01), on 2 February 2018, into a circular sun-synchronous orbit with an altitude of about 507 km in the ionosphere. The CSES-01 has been in orbit for over 6 years, successfully exceeding its designed 5-year lifespan, and will continually operate as long as possible. A second identical successor (CSES-02) will be launched in December 2024 in the same orbit space. The ionosphere is a highly dynamic and complicated system, and it is necessary to comprehensively understand the electromagnetic environment and the physical effects caused by various disturbance sources. The motivation of this report is to introduce the typical electromagnetic waves, mainly in the ELF/VLF band (i.e., ~100 Hz to 25 kHz), recorded by the CSES-01 in order to call the international community for deep research on EM wave activities and geophysical sphere coupling mechanisms. The wave spectral properties and the wave propagation parameters of those typical EM wave activities in the upper ionosphere are demonstrated in this study based on wave vector analysis using the singular value decomposition (SVD) method. The analysis shows that those typical and common natural EM waves in the upper ionosphere mainly include the ionospheric hiss and proton whistlers in the ELF band (below 1 kHz), the quasiperiodic (QP) emissions, magnetospheric line radiations (MLR), the falling-tone lightning whistlers, and V-shaped streaks in the ELF/VLF band (below 20 kHz). The typical artificial EM waves in the ELF/VLF band, such as power line harmonic radiation (PLHR) and radio waves in the VLF band, are also well recorded in the ionosphere. [ABSTRACT FROM AUTHOR]

Accurate wind data are crucial for successful search and rescue (SAR) operations on the sea surface in maritime accidents, as survivors or debris tend to drift with the wind. As maritime accidents frequently occur outside the range of wind stations, SAR operations heavily rely on wind forecasts generated by numerical models. However, numerical models encounter delays in generating results due to spin-up issues, and their predictions can sometimes exhibit inherent biases caused by geographical factors. To overcome these limitations, we reviewed the observations for the first 24 h of the 72-hour forecast from the ECMWF and then post-processed the forecast for the remaining 48 h. By effectively reducing the dimensionality of input variables comprising observation and forecast data using principal component analysis, we improved wind predictions with support vector regression. Our model achieved an average RMSE improvement of 16.01% compared to the original forecast from the ECMWF. Furthermore, it achieved an average RMSE improvement of 5.42% for locations without observation data by employing a model trained on data from the nearest wind station and then applying an adaptive weighting scheme to the output of that model. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) sensors can precisely determine object distances using the pulsed time of flight (TOF) or amplitude-modulated continuous wave (AMCW) TOF methods and velocity using the frequency-modulated continuous wave (FMCW) approach. In this paper, we focus on modelling and analysing the reflection of vector beams (VBs) and vector vortex beams (VVBs) for optical sensing in LiDAR applications. Unlike traditional TOF and FMCW methods, this novel approach uses VBs and VVBs as detection signals to measure the orientation of reflecting surfaces. A key component of this sensing scheme is understanding the relationship between the characteristics of the reflected optical fields and the orientation of the reflecting surface. To this end, we develop a computational model for the reflection of VBs and VVBs. This model allows us to investigate critical aspects of the reflected field, such as intensity distribution, intensity centroid offset, reflectance, and the variation of the intensity range measured along the azimuthal direction. By thoroughly analysing these characteristics, we aim to enhance the functionality of LiDAR sensors in detecting the orientation of reflecting surfaces. [ABSTRACT FROM AUTHOR]

The extent to which soil enzyme activity in assessing soil nutrient availability is useful has been controversial. In this review, we discuss the utility of ecoenzymatic stoichiometry (i.e. the ratio of nutrient- to carbon (C)-acquiring enzyme activities) on the basis of the resource allocation model for ecoenzyme synthesis. Both the selection of appropriate enzymes and the balance between relative amounts of substrates and enzymes in the soil are decisive factors in utilizing the ecoenzymatic stoichiometry. Ecoenzymatic stoichiometry can evaluate the availability of nitrogen (N), phosphorus, and sulfur in many soils in which the enzyme catalytic reactions are substrate-limited but not enzyme-limited. However, the ecoenzymatic stoichiometry approach does not seem to be applicable in soils where microbes are limited by factors other than nutrient availability, such as low temperature, where the enzyme catalytic reactions are enzyme-limited. Certain enzymes, such as N-acetyl-β-glucosaminidase and protease, appear to be insensitive to soil N availability, because they release compounds containing both N and C which serve as sources for both N and C/energy. We propose the use of enzymes such as L-asparaginase and urease as N-acquiring enzymes that release a compound containing N but not C (i.e. NH4+) as the hydrolysis product. Ecoenzymatic stoichiometry can be considered as an indicator of long-term (weeks) temporally integrated soil nutrient availability, rather than instantaneous availability, for plants as well as microbes, because of (i) the long-term persistence of extracellular enzymes in soils; (ii) a significant correlation between ecoenzymatic stoichiometry and the measurements reflecting the quantity of long-term available nutrients in soil; and (iii) a significant correlation between ecoenzymatic stoichiometry and plant nutrient uptake. This review also identifies challenges in assessing microbial nutrient limitation using ecoenzymatic stoichiometry. With a comprehensive understanding of underlying mechanisms and limitations, ecoenzymatic stoichiometry can be used as a biologically relevant indicator of nutrient availability in combination with other approaches such as conventional chemical extraction methods and the nutrient addition approach. [ABSTRACT FROM AUTHOR]

This survey offers a succinct overview of the General Framework of Portfolio Theory (GFPT), consolidating Markowitz portfolio theory, the growth optimal portfolio theory, and the theory of risk measures. Central to this framework is the use of convex analysis and duality, reflecting the concavity of reward functions and the convexity of risk measures due to diversification effects. Furthermore, practical considerations, such as managing multiple risks in bank balance sheets, have expanded the theory to encompass vector risk analysis. The goal of this survey is to provide readers with a concise tour of the GFPT's key concepts and practical applications without delving into excessive technicalities. Instead, it directs interested readers to the comprehensive monograph of Maier-Paape, Júdice, Platen, and Zhu (2023) for detailed proofs and further exploration. [ABSTRACT FROM AUTHOR]

Microbial communities are crucial for water quality and biogeochemical cycling in freshwaters. Microbes secrete extracellular enzymes to decompose organic matter for their needs of nutrients and scarce elements. Yet, there is a lack of knowledge on microbial metabolic limitations in freshwaters, especially in lake sediments. Here, we examined the carbon, nitrogen, and phosphorus-acquiring extracellular enzyme activities and the bacterial and fungal communities of 30 sediments across Xingkai Lake, the largest freshwater lake in Northeast Asia. We further analyzed the microbial metabolic limitations via extracellular enzyme stoichiometry and explored the direct and indirect effects of abiotic and biotic factors on the limitations. We found that microbial metabolisms were primarily limited by phosphorus in Xingkai Lake. For instance, microbial carbon and phosphorus limitations were closely correlated to abiotic factors like water depth, total dissolved solids, sediment total carbon, and conductivity. The metabolic limitations were also affected by biotic factors, such as showing positive relationships with the alpha and beta diversity of bacteria, and with the beta diversity of fungi. In addition, community compositions of bacteria and fungi were mainly correlated to abiotic factors such as total carbon and dissolved organic carbon, respectively. Collectively, microbial metabolic limitations were affected directly or indirectly by abiotic factors and microbial communities. Our findings indicate that microbial metabolic limitations are not only driven by bacteria and fungi but also by abiotic factors such as water depth and total nitrogen, and thus provide empirical evidence for effective management of freshwater lakes under climate warming and intensified human activities. [ABSTRACT FROM AUTHOR]

Background Objective Methods Results Conclusion Growing demand for facial rejuvenation drives advancements in these therapies, including laser, radiofrequency, and focused ultrasound, alongside thermal stimulation adjuncts. These methods, known for stimulating collagen regeneration, skin tightening, and lifting, have gained popularity due to their minimal side effects, low trauma, and high safety, demonstrating favorable outcomes in clinical practice.We sought to assess the efficacy of ultrasound skin tightening for brow lift within the scope of a procedure addressing facial sagging across the entire face. Our aim was to explore a noninvasive method capable of effectively enhancing mild to moderate brow ptosis by tightening and lifting the skin in the upper facial region.This was a rater‐blinded, prospective cohort study. The upper facial region of the participants was treated with the new device, micro‐focused ultrasound (MFU), in model D3.0/D2.0/M3.0. Outcomes of brow lift were measured in comparison of pretreatment and posttreatment photographs and three‐dimensional (3D) vector analysis.A total of 42 participants (37 females) were enrolled, with 2 participants withdrawing from the trial, resulting in 40 subjects who completed 180‐day‐follow‐up and evaluation. 35 (87.5%) were deemed to have clinically significant brow elevation by two blinded assessors (experienced clinicians) at 180‐day posttreatment (p < 0.01). The mean change in brow height after 90‐day was 2.16 ± 0.63 mm at the frontal position (straight‐ahead gaze) (p < 0.01). The 3D vector analysis reveals varying magnitudes of vector displacement in the upward and outward directions of the skin on the frontal region above the eyebrows.Focused ultrasound appears to be a safe and effective method for upper facial skin rejuvenation. A single focused ultrasound treatment on the forehead and temple areas resulted in an average brow elevation of 2.1 mm. [ABSTRACT FROM AUTHOR]

A novel speech feature fusion algorithm with independent vector analysis (IVA) and parallel convolutional neural network (PCNN) is proposed for text-independent speaker recognition. Firstly, some different feature types, such as the time domain (TD) features and the frequency domain (FD) features, can be extracted from a speaker's speech. Generally, the TD and the FD features can be considered as the linear mixtures of independent feature components (IFCs) with an unknown mixing system. To estimate the IFCs, the TD and the FD features of the speaker's speech are concatenated to build the TD and the FD feature matrix, respectively, and then a feature tensor of the speaker's speech is obtained by paralleling the TD and the FD feature matrix. The IVA can be used to estimate the IFC matrices of TD and FD features with the feature tensor. The IFC matrices are utilized as the input of the PCNN to extract the deep features of the TD and FD features, respectively. Finally, the deep features can be integrated to obtain the fusion feature of the speaker's speech for speaker recognition. The experimental results show the effectiveness and performances of the proposed speaker recognition system. [ABSTRACT FROM AUTHOR]

Purpose Methods Results Conclusions The purpose of this study is to compare the reconstructed corneal power (RCP) by working backwards from the post‐implantation spectacle refraction and toric intraocular lens power and to develop the models for mapping preoperative keratometry and total corneal power to RCP.Retrospective single‐centre study involving 442 eyes treated with a monofocal and trifocal toric IOL (Zeiss TORBI and LISA). Keratometry and total corneal power were measured preoperatively and postoperatively using IOLMaster 700. Feedforward neural network and multilinear regression models were derived to map keratometry and total corneal power vector components (equivalent power EQ and astigmatism components C0 and C45) to the respective RCP components.Mean preoperative/postoperative C0 for keratometry and total corneal power was −0.14/−0.08 dioptres and −0.30/−0.24 dioptres. All mean C45 components ranged between −0.11 and −0.20 dioptres. With crossvalidation, the neural network and regression models showed comparable results on the test data with a mean squared prediction error of 0.20/0.18 and 0.22/0.22 dioptres2 and on the training data the neural network models outperformed the regression models with 0.11/0.12 and 0.22/0.22 dioptres2 for predicting RCP from preoperative keratometry/total corneal power.Based on our dataset, both the feedforward neural network and multilinear regression models showed good precision in predicting the power vector components of RCP from preoperative keratometry or total corneal power. With a similar performance in crossvalidation and a simple implementation in consumer software, we recommend implementation of regression models in clinical practice. [ABSTRACT FROM AUTHOR]

To evaluate the visual outcome and astigmatic correction following trifocal intraocular lens (IOL) implantation using the modified femtosecond laser-assisted arcuate keratotomy (FSAK) in Chinese cataract patients with low astigmatism. This retrospective study included consecutive cataract patients with regular corneal astigmatism ranging from 0.75 to 1.5 D who underwent FSAK combined with the trifocal IOL implantation between November 2020 and September 2022. Monocular uncorrected distance visual acuity, uncorrected intermediate visual acuity, uncorrected near visual acuity, and refractive data were collected at the 3-month follow-up. The pre- and post-operative high-order aberrations (HOAs) were recorded. The variation in astigmatism was analyzed using Alpins vector analysis. A total of 27 eyes from 23 patients were analyzed. The monocular uncorrected distance visual acuity (UDVA) (5 m) at the 3-month follow-up was 0.04 ± 0.09 logarithm of the minimum angle of resolution (logMAR), which was significantly improved compared with the preoperative value of 0.95 ± 0.51 logMAR (P <.001). The corneal astigmatism was significantly reduced from 1.24 ± 0.42 D to 0.49 ± 0.34 D (P <.001). The target-induced astigmatism (TIA) was 1.25 ± 0.43 D, the surgically induced astigmatism (SIA) was 1.16 ± 0.52 D, and the difference vector (DV) was 0.5 ± 0.34 D. The magnitude of error (ME) (difference between SIA and TIA) was -0.1 ± 0.41 D, and the correction index (CI) (ratio of SIA to TIA) was 0.93 ± 0.36. The angle of error was 3.92° ± 16.90°. Total HOA was reduced from 0.89 ± 1.11 to 0.41 ± 0.55 (P = 0.184), and the corneal HOA was lowered from 0.17 ± 0.18 to 0.10 ± 0.10 (P = 0.129). Implantation of trifocal IOL following the modified FSAK in Chinese cataract patients exhibited excellent visual efficacy and effectively reduced corneal astigmatism. [ABSTRACT FROM AUTHOR]

Difference methods are widely used for the approximate solution of boundary value problems for partial differential equations. Grid approximations are most simply constructed when the computational domain is divided into rectangular cells. Typically, the grid nodes coincide with the vertices of the cells. In addition to such node-center approximations, grids with nodes at the centers of cells are also used. It is convenient to formulate boundary value problems in terms of invariant operators of vector (tensor) analysis, which are associated with corresponding grid analogs. In this work, analogs of the gradient and divergence operators are constructed on non-standard rectangular grids the nodes of which consist of both the vertices of the computational cells and their centers. The proposed approach is illustrated using approximations of a boundary value problem for a stationary two-dimensional convection–diffusion equation. The key features of constructing approximations for vector problems are discussed with a focus on applied problems of the mechanics of solids. [ABSTRACT FROM AUTHOR]

This paper deals with the extension of principal component analysis, canonical correlation analysis, the Cramer–Rao inequality, and a few other statistical concepts in the real domain to the corresponding complex domain. Optimizations of Hermitian forms under a linear constraint, a bilinear form under Hermitian-form constraints, and similar maxima/minima problems in the complex domain are discussed. Some vector/matrix differential operators are developed to handle the above types of problems. These operators in the complex domain and the optimization problems in the complex domain are believed to be new and novel. These operators will also be useful in maximum likelihood estimation problems, which will be illustrated in the concluding remarks. Detailed steps are given in the derivations so that the methods are easily accessible to everyone. [ABSTRACT FROM AUTHOR]

The objective of this study was to compare the effects of grazing and non-grazing on the physicochemical properties, stoichiometry, ecto-enzyme activities, and microbial element limitations of soils at five depths in subtropical grassland, China, and to identify the influencing factors of enzyme activities and element limitations. Results showed that grazing, soil depth, and the interaction between grazing and depth remarkably changed soil available phosphorus (AP), available potassium (AK), available calcium (ACa) and available magnesium contents (AMg), bulk density (BD), water content (WC), and β-glucosidase activity (βGC) (p = 6.702e-9 - 0.04739). Compared with no g razing, g razing remarkably declined the 0-5 cm soil AP by 73.10% (p = 0.0250), the 0-5, 5-10, 10-20 cm soil WC by 47.19% (p = 0 .0042), 3 7.19% ( p = 0 .0090), a nd 3 0.80% ( p = 0.0034), however, grazing remarkably increased the 0-5, 5-10, 10-20, 20-30 cm soil ACa by 188.76% (p = 7.9e-05), 93.24% (p = 0.0177),84.18% (p = 0.00067), and 38.77% (p = 0.01368), the 0-5, 5-10 cm soil AMg by 186.69% ( p = 0.0016), 78.89% (p = 0.0109), t he 0-5, 5-10, 1 0-20 c m s oil pH b y 0 .51 ( p = 0.0013), 0.37 (p = 0.0006), and 0.27 (p = 0.0114) units, the 0 -5, 5-10, 10-20 cm soil BD by 59.03% ( p = 0.0077), 44.14% ( p = 0.0147), and 35.55% (p = 0.0071), the 30-50 cm soil WC by 22.88% (p = 0.0241), the 0-5 cm soil βGC by 89.49% (p = 0.0011), the 0-5, 5-10 cm soil ACP by 7.87% (p = 0.0300), 6.57% (p = 0.0240), respectively. Grazing exacerbated the microbial C limitation of 0-5 and 10-20 cm soils by 20.51% (p = 0.0078) and 40.38% (p = 0.0209) and switched the soil microbiome from under N limitation to under P limitation at 5-10 cm (p = 0.0390). Specific soil available cations were identified as the important factors that significantly explained the variation of soil ecto-enzyme activities and soil microbial carbon and nutrient limitations. These findings present basic information for the future improvement management of subtropical grassland and understanding the impact of grazing on microbial element limitation. [ABSTRACT FROM AUTHOR]

Introduction: This study is a retrospective case series to compare the accuracy of the Barrett toric calculator using predicted posterior corneal astigmatism (PCA) and PCA measurements using swept-source optical coherence tomography (SS-OCT) and a Scheimpflug camera. This evaluation was conducted across different types of anterior and posterior astigmatism. Methods: A total of 146 eyes from 146 patients implanted with toric intraocular lenses were included. Mean absolute prediction error, standard deviation of prediction error, and the percentage of eyes with prediction errors within ±0.50 diopters (D) were calculated using vector analysis. Biometric measurements were conducted using the IOLMaster 700 and Pentacam HR. A subgroup analysis was conducted based on the orientation of both anterior and posterior corneal astigmatism. Results: The Barrett toric calculator with predicted PCA yielded the best results, with 78.1% having a prediction error ≤ 0.50 D, which was a significantly higher percentage than the Barrett formula with the two versions of measured PCA (P < 0.05). In the subgroup with a horizontally steep meridian PCA using the IOLMaster 700, the Barrett formula with predicted PCA yielded the best results, with 78.3% of cases having a prediction error of less than 0.5 D. This percentage was significantly higher than the other two measured PCA subgroups (P < 0.05). Conclusion: The Barrett toric formula with predicted PCA demonstrated a statistically significantly higher proportion of cases with a prediction error ≤ 0.5 D compared to the two measured PCA formulas (from the IOLMaster 700 or Pentacam). This trend persisted even when the posterior corneal astigmatism was horizontally steep. [ABSTRACT FROM AUTHOR]

Tunable laser sources with a wide wavelength tuning range, mode-hop-free (MHF) operation, and high spectral purity are essential for applications such as high-resolution spectroscopy, coherent detection, and intelligent fiber sensing. In this paper, we present a wide-range tunable laser source that operates without mode hopping, based on external cavity feedback using a semiconductor gain chip as the laser gain medium. The wavelength, power, and spectral characteristics of the laser are experimentally measured. A wide MHF continuous wavelength tuning range from 1480 nm to 1620 nm with a side-mode suppression ratio of more than 61.65 dB is achieved. An output optical power of more than 11.14 dBm with good power stability can also be realized in the full C+L band. This proposed external-cavity tunable laser source features a narrow intrinsic linewidth and MHF tunable radiation with a maximum sweep speed of 200 nm/s, enabling practical applications such as high-resolution vector spectrum analysis. [ABSTRACT FROM AUTHOR]

This study introduces the Orbit Weighting Scheme (OWS), a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval (IR) models, which have traditionally relied on weighting schemes like tf-idf and BM25. These conventional methods often struggle with accurately capturing document relevance, leading to inefficiencies in both retrieval performance and index size management. OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space, emphasizing term relationships and distribution patterns overlooked by existing models. Our research focuses on evaluating OWS's impact on model accuracy using Information Retrieval metrics like Recall, Precision, Interpolated Average Precision (IAP), and Mean Average Precision (MAP). Additionally, we assess OWS's effectiveness in reducing the inverted index size, crucial for model efficiency. We compare OWS-based retrieval models against others using different schemes, including tf-idf variations and BM25Delta. Results reveal OWS's superiority, achieving a 54% Recall and 81% MAP, and a notable 38% reduction in the inverted index size. This highlights OWS's potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS's capabilities in information retrieval methodologies. [ABSTRACT FROM AUTHOR]

One of the special cases of type-2 fuzzy sets is the interval type-2 fuzzy sets, which are less complicated and easier to understand than T2FSs. In this study, we explore the interval type-2 fuzzy linear programming problem with the resources vector that has imprecision of the vagueness type. These types of vagueness are expressed via membership functions. First, we review the three available methods, including the Figueroa and Sarani methods. Then, using the three ideas of Verdegay, Werners, and Guu and Wu for solving fuzzy linear programming problems with vagueness in the resources vector, we propose three new methods for solving interval type-2 fuzzy linear programming problems with vagueness in the resources vector. Finally, we demonstrate the effectiveness of our proposed methods by solving an example and comparing the results obtained with each other and with those of previous methods. [ABSTRACT FROM AUTHOR]

This paper examines the factors influencing Kosovo's economic growth from 2009 to 2022, specifically investigating the relationship between export, capital formation, consumption, and economic growth using co-integration analysis and the Vector Autoregressive Model (VAR). The findings indicate that exports of goods and services, as well as household consumption, negatively affect economic growth. Conversely, gross capital formation positively impacts economic growth. The study underscores the complexity of economic growth, highlighting the varied significance of different determinants in different contexts. Key findings reveal that while export and gross capital formation are significant contributors to economic growth, household consumption shows an insignificant relationship to GDP. This research contributes to the ongoing debate on the critical factors influencing economic growth, providing empirical evidence from the context of Kosovo and enhancing our understanding of these dynamics, thus offering new insights for policymakers. [ABSTRACT FROM AUTHOR]

In this Letter, we have quantified the zones of turbulence in transonic shock diffraction across a 90 ° step corner using Lamb vector analysis. We have analyzed the nature of the three-dimensional turbulence structures using invariants of the velocity gradient tensor. From our results, we conclude that the vortex-induced shock, the lambda shock on the shear layer, and the Kelvin–Helmholtz instability forming on the shear layer are the main contributors in turbulence generation. In our study, we have shown that two-dimensional simulations can resolve the gasdynamic behavior accurately, though three-dimensional simulations are required to understand turbulent structures. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the mean of a random vector based on N independent, identically distributed observations. We prove the existence of an estimator that has a nearoptimal error in all directions in which the variance of the one-dimensional marginal of the random vector is not too small: with probability 1-δ, the procedure returns μ N ​ which satisfies, for every direction u∈Sd-1, ... where σ²(u)=Var(X,u) and C is a constant. To achieve this, we require only slightly more than the existence of the covariance matrix, in the form of a certain moment-equivalence assumption. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of the feature input vector on the performance of dissolved gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree. To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature, 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree algorithms, respectively. Furthermore, the performance of the methods based on best input feature was evaluated and compared with other methods of literature such as Duval triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, the clustering method, and key gases with gas ratio methods. These methods suffer from unreliability, and this is the motivation behind the current work to develop a new technique that enhances the diagnostic accuracy of transformer faults to avoid unwanted faults and outages from the network. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree models, respectively. These diagnostic accuracies are higher than 83.15% for the clustering method, 82.02% for the combined technique, 80.90% for the modified IEC 60599, and 79.78% for key gases with gas ratios, which are the best existing methods. Even if the performance of dissolved gas analysis-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods. [ABSTRACT FROM AUTHOR]