Search Results (15)

Understanding user opinions from user comments or reviews in social media text mining is essential for marketing campaigns and many other applications. However, analyzing social media user comments presents significant challenges due to the complexity of discerning relationships between opinions and aspects, particularly when comments vary greatly in length. To effectively explore aspects and opinions in the sentences, techniques based on mining opinion sentiment of the referred aspects (implicitly or explicitly) in the user comments with ACOS (aspect-category-opinion-sentiment) quadruple extraction have been proposed. Among many others, the opinion tree parsing (OTP) scheme has been shown to be effective and efficient for the ACOS quadruple extraction task in aspect-based sentiment analysis (ABAS). In this study, we continue the efforts to design an efficient ABSA scheme. We extend the original OTP scheme further with richer context parsing rules, utilizing conjunctions and semantic modifiers to provide more context information in the sentence and thus effectively improving the accuracy of the analysis. Meanwhile, regarding the limitations of computation resources for edge devices in edge computing scenario, we also investigate the trade-off between computation saving (in terms of the percentage of model parameters to be updated) and the model’s performance (in terms of inference accuracy) on the proposed scheme under PEFT (parameter-efficient fine-tuning). We evaluate the proposed scheme on publicly available ACOS datasets. Experiment results show that the proposed enhanced OTP (eOTP) model improves the OTP scheme both in precision and recall measurements on the public ACOS datasets. Meanwhile, in the design trade-off evaluation for resource-constrained devices, the experiment results show that, in model training, eOTP requires very limited parameters (less than 1%) to be retrained by keeping most of the parameters frozen (not modified) in the fine-tuning process, at the cost of a slight performance drop (around 4%) in F1-score compared with the case of full fine-tuning. These demonstrate that the proposed scheme is efficient and feasible for resource-constrained scenarios such as for mobile edge/fog computing services. Full article

Extreme-mass-ratio inspirals (EMRIs) are promising gravitational-wave (GW) sources for space-based GW detectors. EMRI signals typically have long durations, ranging from several months to several years, necessitating highly accurate GW signal templates for detection. In most waveform models, compact objects in EMRIs are treated as test particles without accounting for their spin, mass quadrupole, or tidal deformation. In this study, we simulate GW signals from EMRIs by incorporating the spin and mass quadrupole moments of the compact objects. We evaluate the accuracy of parameter estimation for these simulated waveforms using the Fisher Information Matrix (FIM) and find that the spin, tidal-induced quadruple, and spin-induced quadruple can all be measured with precision ranging from ${10}^{-2}$ to ${10}^{-1}$, particularly for a mass ratio of ∼${10}^{-4}$. Assuming the “true” GW signals originate from an extended body inspiraling into a supermassive black hole, we compute the signal-to-noise ratio (SNR) and Bayes factors between a test-particle waveform template and our model, which includes the spin and quadrupole of the compact object. Our results show that the spin of compact objects can produce detectable deviations in the waveforms across all object types, while tidal-induced quadrupoles are only significant for white dwarfs, especially in cases approaching an intermediate-mass ratio. Spin-induced quadrupoles, however, have negligible effects on the waveforms. Therefore, our findings suggest that it is possible to distinguish primordial black holes from white dwarfs, and, under certain conditions, neutron stars can also be differentiated from primordial black holes. Full article

Thorium is a radionuclide used in various environmental studies such as dating, sediment movement, soil–plant transfer studies, and contamination of waste from the natural fuel cycle. The liquid–liquid extraction method using tri-n-butyl phosphate (TBP) allows for the separation of Th from the accompanying actinides. However, the separation of Th and U present in the same sample is not trivial. This separation is influenced by the starting acid (HCl or HNO₃), the concentration of TBP in an organic solvent, and the concentration of the acid used for re-extracting Th, which is typically HCl. Therefore, it is necessary to study these factors to ensure that the method has sufficient chemical yield and selectivity in complex matrices. This study presents a systematic investigation of the aforementioned parameters, making the necessary variations to select an optimal method for the radiochemical separation of Th. The ideal conditions were obtained using 4 M HCl as the acid prior to extraction, a 1:4 solution of TBP in xylene, and 4 M HCl as the re-extracting agent. The accuracy and precision were studied in four intercomparison exercises conducted in quadruplicate, using the parameters E_numbers, RB(%), and RSD(%) for ²³²Th and ²³⁰Th. The sensitivity of the method was experimentally studied and the limit of detection (LoD) was determined according to ISO 11929:2005. Additionally, the linearity of the method showed that the experimental and theoretical activity concentrations of ²³²Th and ²³⁰Th had slopes of 1 with an intercept close to 0. Full article

Wildfires, which are distinguished by their destructive nature and challenging suppression, present a significant threat to ecological environments and socioeconomic systems. In order to address this issue, the development of efficient and accurate fire detection technologies for early warning and timely response is essential. This paper addresses the complexity of forest and mountain fire detection by proposing YOLO-CSQ, a drone-based fire detection method built upon an improved YOLOv8 algorithm. Firstly, we introduce the CBAM attention mechanism, which enhances the model’s multi-scale fire feature extraction capabilities by adaptively adjusting weights in both the channel and spatial dimensions of feature maps, thereby improving detection accuracy. Secondly, we propose an improved ShuffleNetV2 backbone network structure, which significantly reduces the model’s parameter count and computational complexity while maintaining feature extraction capabilities. This results in a more lightweight and efficient model. Thirdly, to address the challenges of varying fire scales and numerous weak emission targets in mountain fires, we propose a Quadrupled-ASFF detection head for weighted feature fusion. This enhances the model’s robustness in detecting targets of different scales. Finally, we introduce the WIoU loss function to replace the traditional CIoU object detection loss function, thereby enhancing the model’s localization accuracy. The experimental results demonstrate that the improved model achieves an mAP@50 of 96.87%, which is superior to the original YOLOV8, YOLOV9, and YOLOV10 by 10.9, 11.66, and 13.33 percentage points, respectively. Moreover, it exhibits significant advantages over other classic algorithms in key evaluation metrics such as precision, recall, and F1 score. These findings validate the effectiveness of the improved model in mountain fire detection scenarios, offering a novel solution for early warning and intelligent monitoring of mountain wildfires. Full article

Forest management is the fundamental approach to continuously improve forest quality and achieve the quadruple functions of forests. The identification of forest management types is the basis of forest management and a key technical link in the formulation of forest management plans. However, due to insufficient application of forestry informatization and digitization, there are problems in the organization and application of management types, such as inaccurate identification, diversified standards, long organizational cycles, and low decision-making efficiency. Typical technical models are difficult to widely promote and apply. To address these challenges, this study proposes the Stacking Ensemble Forest Management Type Identification (SEFMTI) method based on Stacking ensemble learning. Initially, four typical forest management types from the sustainable forest management pilot of the Yichun Forestry Group were selected as research subjects, and 19 stand parameters were chosen to form the research data, training various recognition models. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest (RF) methods were used to analyze key decision-making indicators for forest management type recognition and compare the performance of different models. The results show that (1) the SEFMTI model achieved an accuracy rate of 97.14%, effectively improving the accuracy of forest management type recognition while ensuring stability; (2) average age (AG), age group (AGG), crown density (CD), and stand origin (SO) are key decision-making indicators for recognizing forest management types; and (3) after feature selection, the SEFMTI model significantly enhanced the efficiency of model training while maintaining a high accuracy rate. The results validate the feasibility of the SEFMTI identification method, providing a basis for the gradual implementation of sustainable forest management pilots and aiding in the precise improvement of forest quality. Full article

We explore second-order systems of non-stiff initial-value problems (IVPs), particularly those cases where the first derivatives are absent. These types of problems are of significant interest and have applications in various domains, such as astronomy and physics. Runge–Kutta–Nyström (RKN) pairs stand out as highly effective methods of addressing these IVPs. In order to create a pair with eighth and sixth orders, we need to address a certain known set of equations concerning the coefficients. When constructing such pairs for use in double-precision arithmetic, we often need to meet various conditions. Primarily, we aim to maintain small coefficient magnitudes to prevent a loss of accuracy. Nevertheless, in the context of quadruple precision, we can tolerate larger coefficients. This flexibility enables us to establish pairs with eighth and sixth orders that exhibit significantly reduced truncation errors. Traditionally, these pairs are constructed to go through eight stages per step. Here, we propose using nine stages per step. Then we have available more coefficients in order to further reduce truncation errors. As a result, we construct a novel pair that, as anticipated, achieves superior performance compared to equivalent-order pairs in various significant problem scenarios. Full article

The red palm weevil (RPW, Rhynchophorus ferrugineus) is an invasive and highly destructive pest that poses a serious threat to palm plants. To improve the efficiency of adult RPWs’ management, an enhanced YOLOv5 object detection algorithm based on an attention mechanism is proposed in this paper. Firstly, the detection capabilities for small targets are enhanced by adding a convolutional layer to the backbone network of YOLOv5 and forming a quadruple down-sampling layer by splicing and down-sampling the convolutional layers. Secondly, the Squeeze-and-Excitation (SE) attention mechanism and Convolutional Block Attention Module (CBAM) attention mechanism are inserted directly before the SPPF structure to improve the feature extraction capability of the model for targets. Then, 2600 images of RPWs in different scenes and forms are collected and organized for data support. These images are divided into a training set, validation set and test set following a ratio of 7:2:1. Finally, an experiment is conducted, demonstrating that the enhanced YOLOv5 algorithm achieves an average precision of 90.1% (mAP@0.5) and a precision of 93.8% (P), which is a significant improvement compared with related models. In conclusion, the enhanced model brings a higher detection accuracy and real-time performance to the RPW-controlled pest pre-detection system, which helps us to take timely preventive and control measures to avoid serious pest infestation. It also provides scalability for other pest pre-detection systems; with the corresponding dataset and training, the algorithm can be adapted to the detection tasks of other pests, which in turn brings a wider range of applications in the field of monitoring and control of agricultural pests. Full article

The second-order system of non-stiff Initial Value Problems (IVP) is considered and, in particular, the case where the first derivatives are absent. This kind of problem is interesting since since it arises in many significant problems, e.g., in Celestial mechanics. Runge–Kutta–Nyström (RKN) pairs are perhaps the most successful approaches for solving such type of IVPs. To achieve a pair attaining orders eight and six, we have to solve a well-defined set of equations with respect to the coefficients. Here, we provide a simplified form of these equations in a robust algorithm. When creating such pairings for use in double precision arithmetic, numerous conditions are often satisfied. First and foremost, we strive to keep the coefficients’ magnitudes small to prevent accuracy loss. We may, however, allow greater coefficients when working with quadruple precision. Then, we may build pairs of orders eight and six with significantly smaller truncation errors. In this paper, a novel pair is generated that, as predicted, outperforms state-of-the-art pairs of the same orders in a collection of important problems. Full article

High algebraic order Runge–Kutta embedded methods are commonly used when stringent tolerances are demanded. Traditionally, various criteria are satisfied while constructing these methods for application in double precision arithmetic. Firstly we try to keep the magnitude of the coefficients low, otherwise we may experience loss of accuracy; however, when working in quadruple precision we may admit larger coefficients. Then we are able to construct embedded methods of orders eight and seven (i.e., pairs of methods) with even smaller truncation errors. A new derived pair, as expected, is performing better than state-of-the-art pairs in a set of relevant problems. Full article

A simple approximation scheme to describe the half width of the Voigt profile as a function of the relative contributions of Gaussian and Lorentzian broadening is presented. The proposed approximation scheme is highly accurate and provides an accuracy better than 10⁻¹⁷ for arbitrary α_L/α_G ratios. In particular, the accuracy reaches an astonishing 10⁻³⁴ (quadruple precision) in the domain 0 ≤ α_L/α_G ≤ 0.2371 ∪ α_L/α_G ≥ 33.8786. Full article

With the fast advances in computational sciences, there is a need for more accurate computations, especially in large-scale solutions of differential problems and long-term simulations. Amid the many numerical approaches to solving differential problems, including both local and global methods, spectral methods can offer greater accuracy. The downside is that spectral methods often require high-order polynomial approximations, which brings numerical instability issues to the problem resolution. In particular, large condition numbers associated with the large operational matrices, prevent stable algorithms from working within machine precision. Software-based solutions that implement arbitrary precision arithmetic are available and should be explored to obtain higher accuracy when needed, even with the higher computing time cost associated. In this work, experimental results on the computation of approximate solutions of differential problems via spectral methods are detailed with recourse to quadruple precision arithmetic. Variable precision arithmetic was used in Tau Toolbox, a mathematical software package to solve integro-differential problems via the spectral Tau method. Full article

Glucosinolates are biologically active secondary metabolites in Brassicaceae plants that play a critical role in positive and negative interactions between plants and root-associated microbial communities. The aim of this study was to develop a reversed-phase liquid chromatography method to quantify and identify intact glucosinolates in the root of Arabidopsis thaliana (Arabidopsis) grown in non-sterile natural soil by using liquid chromatography-hybrid triple quadruple-linear ion trap (LC-QqQ(LIT)) mass spectrometry. The Synergi Fusion C18-based column was found to be effective for sufficient retention and separation of nine intact glucosinolates without the need for time-consuming desulfation or ion-pairing steps. Method validation results showed satisfactory inter-day and intra-day precision for all glucosinolates except for 4-hydroxyglucobrassicin. Good inter-day and intra-day accuracy and recovery results were observed for glucoiberin, gluconapin, glucobrassicin, 4-methoxyglucobrassicin and neoglucobrassicin. However, for 4-hydroxyglucobrassicin, glucoraphanin and glucoerucin corrections to quantification results might be necessary since the recovery and accuracy results were not optimal. Matrix effects were shown to have a negligible effect on the ionization of all target analytes. The established liquid chromatography–tandem mass spectrometry (LC-MS/MS) method was applied to quantify target intact glucosinolates in Arabidopsis root crude extract of four different wild-type accessions where differences in terms of concentration and composition of intact glucosinolates were observed. Employment of sensitive and selective precursor ion survey scan of m/z 97 in combination with the information-dependent acquisition (IDA) of the enhanced product ion (EPI) dependent scan (Prec97-IDA-EPI) using LC-QqQ(LIT) provided high confidence in structural characterization of diverse intact glucosinolate profiles in complex Arabidopsis root crude extract. Full article

The rapid identification and quantitation of alkaloids produced by Epichloë endophyte-infected pasture grass is important for the agricultural industry. Beneficial alkaloids, such as peramine, provide the grass with enhanced insect protection. Conversely, ergovaline and lolitrem B can negatively impact livestock. Currently, a single validated method to measure these combined alkaloids in planta does not exist. Here, a simple two-step extraction method was developed for Epichloë-infected perennial ryegrass (Lolium perenne L.). Peramine, ergovaline and lolitrem B were quantified using liquid chromatography–mass spectrometry (LC–MS). Alkaloid linearity, limit of detection (LOD), limit of quantitation (LOQ), accuracy, precision, selectivity, recovery, matrix effect and robustness were all established. The validated method was applied to eight different ryegrass-endophyte symbiota. Robustness was established by comparing quantitation results across two additional instruments; a triple quadruple mass spectrometer (QQQ MS) and by fluorescence detection (FLD). Quantitation results were similar across all three instruments, indicating good reproducibility. LOQ values ranged from 0.8 ng/mL to 6 ng/mL, approximately one hundred times lower than those established by previous work using FLD (for ergovaline and lolitrem B), and LC–MS (for peramine). This work provides the first highly sensitive quantitative LC–MS method for the accurate and reproducible quantitation of important endophyte-derived alkaloids. Full article

Anthropogenic mineral residues are characterized by their material complexity and heterogeneity, which pose challenges to the chemical analysis of multiple elements. However, creating an urban mine knowledge database requires data using affordable and simple chemical analysis methods, providing accurate and valid results. In this study, we assess the applicability of simplified multi-element chemical analysis methods for two anthropogenic mineral waste matrices: (1) lithium-ion battery ash that was obtained from thermal pre-treatment and (2) rare earth elements (REE)-bearing iron-apatite ore from a Swedish tailing dam. For both samples, simplified methods comprising ‘in-house’ wet-chemical analysis and energy-dispersive X-ray fluorescence (ED-XRF) spectrometry were compared to the results of the developed matrix-specific validated methods. Simplified wet-chemical analyses showed significant differences when compared to the validated method, despite proven internal quality assurance, such as verification of sample homogeneity, precision, and accuracy. Matrix-specific problems, such as incomplete digestion and overlapping spectra due to similar spectral lines (ICP-OES) or element masses (ICP-MS), can result in quadruple overestimations or underestimation by half when compared to the reference value. ED-XRF analysis proved to be applicable as semi-quantitative analysis for elements with mass fractions higher than 1000 ppm and an atomic number between Z 12 and Z 50. For elements with low mass fractions, ED-XRF analysis performed poorly and showed deviations of up to 90 times the validated value. Concerning all the results, we conclude that the characterization of anthropogenic mineral residues is prone to matrix-specific interferences, which have to be addressed with additional quality assurance measures. Full article

We present examples of Padé approximations of the $\alpha$ -effect and eddy viscosity/diffusivity tensors in various flows. Expressions for the tensors derived in the framework of the standard multiscale formalism are employed. Algebraically, the simplest case is that of a two-dimensional parity-invariant six-fold rotation-symmetric flow where eddy viscosity is negative, indicating intervals of large-scale instability of the flow. Turning to the kinematic dynamo problem for three-dimensional flows of an incompressible fluid, we explore the application of Padé approximants for the computation of tensors of magnetic $\alpha$ -effect and, for parity-invariant flows, of magnetic eddy diffusivity. We construct Padé approximants of the tensors expanded in power series in the inverse molecular diffusivity $1/\eta$ around $1/\eta =0$ . This yields the values of the dominant growth rate to satisfactory accuracy for $\eta$ , several dozen times smaller than the threshold, above which the power series is convergent. We do computations in Fortran in the standard “double” (real*8) and extended “quadruple” (real*16) precision, and perform symbolic calculations in Mathematica. Full article