Search Results (5,288)

This paper addresses the challenge of deduplicating job postings in large, heterogeneous datasets by introducing an efficient, multi-stage methodology that combines embedding-based filtering with Large Language Model (LLM) validation. The proposed system begins with data preprocessing and semantic vectorization of key textual fields using a text embedding model. To reduce the computational cost of exhaustive pairwise comparisons, a clustering-based grouping mechanism is employed to restrict comparisons to semantically coherent clusters. Candidate duplicates are then validated using LLMs, which assess semantic equivalence across highlighted differences in job titles, descriptions, companies, and locations. The proposed system is evaluated on an augmented dataset of 50,000 job postings, producing 6669 candidate pairs for validation. Among the evaluated models, GPT-4o achieved the highest F1-score (95.10%), while the lightweight Phi-4 model demonstrated competitive performance (92.58%) with significantly lower computational cost. These findings demonstrate that the proposed hybrid framework achieves high semantic precision while remaining scalable for continuous large-scale deployment. Full article

Plant nitrogen content (PNC) is a core physiological parameter characterizing crop nitrogen nutrition status. Its precise and dynamic monitoring is crucial for crop growth diagnosis, optimizing nitrogen fertilizer management, enhancing fertilizer use efficiency, and reducing agricultural nonpoint source pollution. This study utilized multispectral imagery from unmanned aerial vehicles (UAVs) to extract vegetation indices (VIs) and texture feature values (TFVs) during critical growth stages of alfalfa. By combining TFVs to construct texture indices (TIs), variables exhibiting extremely significant correlations with alfalfa PNC (p < 0.001) were identified. We used VIs, TIs, and their combined features as model inputs. The performance of four machine learning models—random forest regression (RFR), Support Vector Regression (SVR), Backpropagation Neural Network (BPNN), and gradient boosting (XG-Boost)—was comprehensively assessed for estimating alfalfa PNC. Our results indicate the following: (1) The correlation coefficients |r| between VIs and alfalfa PNC ranged from 0.56 to 0.68; TIs constructed from TFVs significantly enhanced PNC correlation compared to raw texture values, with |r| exceeding 0.6. (2) Integrating VIs and TIs substantially improved the accuracy of PNC estimation models across growth stages. Compared to using VIs or TIs alone, the validation set R² increased by 5.4–19.7%, 1.7–16.4%, and 5.2–17.2% for the branching, budding, and initial flowering stages, respectively. (3) The XG-Boost model demonstrated optimal performance across all growth stages and input variables. Particularly during the budding stage, the VIs + TIs model achieved the highest fitting accuracy: training set R² = 0.81, RMSE = 0.15%; validation set R² = 0.80, RMSE = 0.12%. In summary, integrating multispectral vegetation indices and texture indices effectively enhances the accuracy of PNC estimation in alfalfa, providing scientific support for precision field management and fertilization decisions in alfalfa cultivation. Full article

Estimating individual tree Above-Ground Biomass (AGB) is essential for assessing ecological functions and carbon storage in both forest and urban environments. Traditional field-based methods, such as plot measurements, are costly and impractical for large-scale applications. However, satellite- and aerial-based techniques lack the spatial resolution for individual-tree-level analysis. Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) data, combined with machine learning (ML), offers a powerful alternative for detailed tree structure measurement and AGB estimation. Leveraging advances in deep-learning-based individual tree detection and geometric structure estimation including Height (H), Surface Area (SA), Volume (V), and Crown Width (CW), this study develops ML regression models for estimating individual tree AGB. We explore three objectives: (1) evaluating four regression models including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Feed-Forward Neural Network (FFNN); (2) sensitivity assessment of different geometric feature combinations on model accuracy; and (3) improving model robustness using Synthetic Minority Over-sampling Technique (SMOTE) data augmentation for addressing imbalanced data. Results show that the RF model outperforms others that achieved the lowest RMSE and most balanced residual distribution. CW was the strongest single predictor of AGB and, in combination with H, yielded to the most accurate results. This combination improved RMSE and R² by 14.2% and 89.3% with respect to single-variable-based models. The integration of SMOTE and RF further improved model performance since it lowered RMSE by 225.6 kg (~22.1%) and increased R² by 0.76 (~49.0%). This was particularly evident in underrepresented low and high AGB ranges. The proposed RF-SMOTE approach is a cost-effective and scalable approach for generating high-quality ground truth data to enable large-scale satellite-based biomass estimation and help forest carbon accounting and planning in cities and forests. Full article

Rapid, non-destructive estimation of leaf chlorophyll content (SPAD) is crucial for assessing plant photosynthetic health and nutrient status. However, conventional methods rely on specialized instruments (e.g., SPAD meters and hyperspectral sensors) which are costly, cumbersome, or unsuitable for large-scale field deployment. While RGB image analysis offers a low-cost alternative, most existing approaches depend solely on color features, which are susceptible to environmental interference and lack robustness across growth stages. To address these limitations, this study proposes a novel machine learning framework that fuses both color and texture features from smartphone-captured RGB images for accurate SPAD estimation in walnut seedlings and explores its linkage with potassium nutrition. ‘Wen 185’ walnut seedlings were subjected to seven potassium concentration treatments to induce a chlorophyll gradient. From the leaf images, 22 color indices and 8 texture features based on the Gray-Level Co-occurrence Matrix (GLCM) were extracted. Prediction models were built and compared using Random Forest (RF), XGBoost, and a Support Vector Machine (SVM), with two fusion strategies: data-level and feature-level fusion. Results demonstrated that the RF model with feature-level fusion achieved optimal performance (validation set: R² = 0.939, RMSE = 0.014, and RPD = 4.539), significantly outperforming models using single-feature types. SHAP analysis identified normalized red, normalized blue, and green-band correlation as the most influential features. This work fills a critical gap by establishing a robust, cost-effective, and interpretable method for SPAD monitoring using ubiquitous RGB imagery. Furthermore, the strong correlation between image-predicted SPAD and potassium levels confirms the method’s high potential for early and non-destructive diagnosis of potassium deficiency in orchard management. Full article

This paper develops a structural and constructive theory of right generalized quasi-polycyclic (GQPC) codes over the finite chain ring $R={\mathbb{F}}_q+u{\mathbb{F}}_q$ with $u^2=0$, extending the existing field-based GQPC framework to a ring-theoretic setting. Right GQPC codes over R are modeled as $R\left[x\right]$-submodules of direct products of polycyclic ambient algebras $R\left[x\right]/⟨x^{e_i}-{\alpha }_i\left(x\right)⟩$, induced by vectors ${\alpha }_i\in R^{e_i}$, thereby unifying right quasi-polycyclic and generalized quasi-cyclic codes over $R.$ Under explicit and verifiable factorization conditions on the defining polynomials, we establish a Chinese Remainder Theorem decomposition that reduces right GQPC codes to collections of shorter codes over finite chain-ring extensions of R. This decomposition yields a characterization of ρ-generator right GQPC codes and leads to a canonical normalized generating set with an upper-triangular structure. As a consequence, we obtain an explicit rank formula in terms of the diagonal generator polynomials, together with an effective normalization algorithm. To demonstrate the coding-theoretic impact of the framework, we combine these structural results with a distance-compatible Gray map $\Phi :R\to {\mathbb{F}}_q^2$ and construct new q-ary linear codes from 2-generator right GQPC codes of index 2 over R. For $q=9$ and $q=3$, the resulting Gray images attain optimal or near-optimal parameters with respect to the best-known bounds, confirming that right GQPC codes over ${\mathbb{F}}_q+u{\mathbb{F}}_q$ constitute a robust and effective ring-based source of high-quality linear codes. Full article

In this paper, a sensorless field-oriented vector control (FOC) strategy combining an improved adaptive sliding mode observer (IASMO) and a finite-position-set phase-locked loop (FPS-PLL) is proposed for a surface permanent magnet synchronous motor (SPMSM) operating in the medium- and high-speed range. Firstly, a sliding mode observer (SMO) that can realize the observation of back electromotive force (back-EMF) is proposed, and an adaptive reaching law that can reduce the sliding mode coefficient is designed to help the SMO observe the back-EMF for the purpose of reducing chattering as well as verifying the stability of the system. Then, the FPS-PLL is used instead of a phase-locked loop (PLL) to extract the rotor position information from the observed back-EMF, thus avoiding the time-consuming process of tuning the PI parameters. The proposed FPS-PLL reduces the number of iterations from 64 to 20 while maintaining effective estimation performance. Finally, the effectiveness of the proposed scheme in suppressing chattering and maintaining comparable estimation accuracy while reducing computational burden is demonstrated by experiments. Full article

Background/Objectives: Recombinant adeno-associated virus (AAV) vectors have revolutionized gene therapy for monogenic diseases such as Duchenne muscular dystrophy (DMD). However, high systemic doses required for muscle transduction cause a spectrum of toxicities ranging from transient hepatic inflammation to fatal multi-organ failure leading to death. These adverse events have reshaped the risk–benefit considerations for gene therapy in DMD. Methods: We conducted a narrative review describing complications associated with AAV-mediated gene therapies in the DMD field. PubMed and Clinicaltrials databases were used to search for peer-reviewed manuscripts published between 1987 and 2025. Publicly available abstracts and press releases were also used to describe AAV-mediated adverse events that have been discovered. Priority was given to large prospective cohorts, meta-analyses, and high-impact publications. Results: We outlined the mechanistic basis of AAV toxicity—spanning innate and adaptive immune activation, vector–host interactions, transgene overexpression, and host vulnerability—and discussed their therapeutic implications for DMD. We also highlighted ongoing strategies for vector re-design, immune modulation, patient selection, and regulatory adaptation, aiming to improve efficacy with safety in the next generation of muscular dystrophy gene therapies. Conclusions: Patient safety remains the number one priority in the AAV-mediated gene therapies field. Achieving long-term benefits requires continued optimization of existing vectors, implementation of strict criteria for patient selection, and regulation of immune responses, with close collaboration and transparent dialog among scientists, clinicians, and regulatory agencies, informed by both successful cases as well as tragic deaths reported in the fields of neuromuscular diseases. Full article

Dynamical systems with chaotic attractors are an interesting topic not only for their complex behavior but also due to their potential applications. Along with the chaos, systems can also present interesting features such as multistability, global basin of attractions, entangled basins of attraction, etc. The existence of chaotic systems with multistable hidden attractors increases complexity but also the number of potential applications. Several systems with hidden attractors have already been found by numerical search; however, it is usually not possible to substantially modify their equations or attractor geometry. In this study, an approach to generate multistable systems with a class of hidden attractors is proposed. The approach allows for the control of the amplitude and frequency of the chaotic signals of the different attractors as well as their location in the space by preserving a simple matrix form in the vector field. Particular cases with mono-stability and multistability are shown. Also, chaotic signals obtained through the approach are used in a pseudorandom number generator to obtain binary sequences which are tested under the Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications provided by the National Institute of Standards and Technology (NIST). Full article

Matched Field Processing (MFP) suffers from environmental mismatch in deep-sea long-range source localization. Although Frequency Difference Matched Field Processing (FDMFP) improves mismatch tolerance, it fails due to caustic phase effects. Frequency Difference Source Localization (FDSL) effectively compensates for caustic phase errors by applying frequency-difference processing to both the measured field and the replica field. However, conventional FDSL typically relies on large-aperture arrays with numerous elements, resulting in high deployment costs and bulky systems. Furthermore, it exhibits limited resolution and elevated sidelobes. These limitations are exacerbated under reduced element counts and low signal-to-noise ratio (SNR) conditions. To improve performance under low SNR and small-array configurations, this paper proposes the FDSL-MVDR and FDSL-MUSIC methods by deriving adaptive weight vectors based on the frequency-difference covariance structure and redefining the ambiguity surface. Numerical simulations in a deep-sea Munk environment (source range 195 km, depth 1000 m) using a 15-element vertical line array demonstrate that the adaptive FDSL methods outperform conventional FDSL in terms of peak sharpness and sidelobe suppression. FDSL-MUSIC achieves approximately 100% localization success at SNR = −5 dB, a 4 dB improvement over conventional FDSL. Performance analyses under representative environmental mismatches indicate that the adaptive FDSL methods maintain robust localization performance and high-resolution characteristics in complex deep-sea environments. These results validate the feasibility of high-precision deep-sea localization using a few-element array. Full article

Orchid pollination is traditionally considered to rely on intact pollinarium transfer by animal vectors. Species lacking a functional viscidium are generally classified as obligately autogamous. In this study, we investigated the reproductive biology of Epipactis bugacensis, a taxon long regarded as strictly self-pollinating. Floral visitor activity was assessed through repeated field observations, and pollinator dependence was tested using a pollinator-exclusion (net-covering) experiment at two Hungarian populations, combined with measurements of fruit set, capsule volume, seed number, and seed density. We documented a previously unreported pollen-transfer mechanism in E. bugacensis, whereby halictid bees fragment pollinia and transfer these fragments in their scopa to neighboring flowers enabling geitonogamous deposition and suggesting the potential for xenogamous pollen transfer. Other visitor taxa showed no evidence of effective pollen transport. Mesh coverage increased fruit set, capsule volume, and seed number, while seed density remained unchanged. Reproductive output declined from basal to apical positions along flowering shoots, revealing strong internal resource-allocation constraints. Overall, E. bugacensis is predominantly self-pollinating but not strictly obligate autogamous, and its reproductive success is governed primarily by microhabitat quality rather than pollinator availability. Full article

This systematic review summarizes the application of artificial intelligence (AI) in classifying upper-limb motion using Electroencephalogram (EEG) and Electromyogram (EMG) signals, focusing on the field’s progression from Traditional Machine Learning (TML) to Deep Learning (DL) architectures. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a search of PubMed, IEEEXplore, and Web of Science yielded 301 eligible studies published up to June 2025. The results indicate a change from classical classifiers like Linear Discriminant Analysis (LDA) and Support Vector Machines (SVMs) toward DL approaches. While Convolutional Neural Networks (CNNs) remain the most frequently implemented, emerging architectures, including Long Short-Term Memory (LSTM) networks and Transformers, have demonstrated remarkable performance. Despite the rise of DL, classical models remain highly relevant due to their robustness and efficiency. This review also identifies a heavy reliance on EEG-only modalities (60%), with only 7% of studies utilizing hybrid EEG-EMG systems, representing a potential missed opportunity for signal fusion. Full article

Land conversion due to deforestation and urbanization tends to change oasis ecosystem service in arid and semiarid regions. In this context, this paper examines the impacts of land use/land cover change (LULC) on the degradation of ecosystem service values (ESVs) in AlUla cultural oasis, northwestern Saudi Arabia, using Landsat images of the years 1984, 1992, 2010, and 2023, cross-validated with field surveys and high-resolution data. Different approaches were used for the purpose of this study, including support vector machine (SVM), hot-spot analysis, and cluster and outlier analysis (local Moran’s I). However, to compute and evaluate the ESV, we used the benefit transfer approach (BTM). The results indicated a significant change in the built-up area between 1984 and 2023, which increased by 12.53 km². This transformation led to a wide variation in all ESVs each year, with an increase of ESV by USD 44.78 million during 1984 to 1992. In the following decade, however, the AlUla oasis witnessed a loss in its ESV by approximately USD 0.73 million and USD 36.70 million during the periods 1992 to 2010 and 2010 and 2023, respectively. Moreover, the spatial distribution patterns of ESVs varied considerably, especially for provisioning service (PS) and supporting service (SS), while it was more clustering for regulating service (RS) and cultural service (CS). This study indicates that urban development is among the important factors behind changes and losses in the ESV in this arid oasis. Full article

The increasing prevalence of insecticide resistance threatens the efficacy of Integrated Mosquito Management (IMM) programs, particularly in regions reliant on chemical control for vector-borne disease prevention. Cross-resistance between active ingredients severely complicates essential resistance management strategies like product rotation. The previous literature suggests that laboratory-induced S-methoprene-resistant Culex species may be somewhat cross-resistant to pyriproxyfen, another juvenile hormone analog. This is a critical concern in the Chicago, IL, USA metropolitan area, where pyriproxyfen is used against mosquitoes with reduced susceptibility to S-methoprene. To determine if S-methoprene-resistant Culex pipiens are cross-resistant to pyriproxyfen in nature, we assessed 31 field-collected populations with significant S-methoprene exposure but varying histories of pyriproxyfen use by dose–response bioassays. Culex pipiens from all 31 sites exhibited high resistance to S-methoprene (RR₅₀ > 10), and 84% were at least moderately resistant to pyriproxyfen (RR₅₀ > 5). Reduced susceptibility to pyriproxyfen was confirmed in pyriproxyfen-unexposed populations, demonstrating potential S-methoprene-mediated cross-resistance. The level of S-methoprene resistance and the level of pyriproxyfen exposure significantly correlated with the level of pyriproxyfen resistance. We report the first widespread, high-level pyriproxyfen resistance in any medically significant mosquito species, underscoring the critical need for routine resistance surveillance and the adoption of integrated resistance management tactics utilizing larvicides with distinct modes of action. Full article

The extremely large-scale antenna array (ELAA) is recognized as a promising technology for the sixth-generation wireless communication systems. Besides the extended near-field region, the enlarged aperture introduces spatial non-stationarity, which is characterized by the visibility region (VR). When all the antenna elements in the ELAA are used indiscriminately, the spatial non-stationarity can result in the user receiving signals radiated by partial antenna elements, which cannot be ignored in designing an effective multiple access scheme. To address this, a rate-splitting multiple access (RSMA) scheme is designed for the ELAA with spatial non-stationarity in this paper, where antenna selection and RSMA are jointly exploited to alleviate the effect of the spatial non-stationarity. Then, an optimization problem (OP) is formulated to maximize the weighted sum-rate (WSR) by jointly optimizing user grouping, digital precoding, and the rate-splitting vector. To solve the formulated OP, antenna selection is initially performed, followed by the user grouping algorithm. Subsequently, given the user grouping result, the conditional optimal solutions are obtained by using the semidefinite relaxation method. Simulation results demonstrate that the proposed scheme achieves a higher WSR than the baseline schemes. Full article

This study develops and evaluates machine learning models for classifying Korean Baseball Organization (KBO) playoff advancement using pitching metrics from 2015 to 2024 (N = 100 team-seasons), focusing specifically on pitching’s contribution to playoff qualification to address the ERA-FIP paradox at the team level. Five algorithms were compared: Random Forest, Support Vector Machines, Logistic Regression, Neural Networks, and Decision Trees. Independent variables included ten pitching statistics: Earned Run Average (ERA), Walks and Hits per Inning Pitched (WHIP), Fielding Independent Pitching (FIP), Strikeouts per 9 Innings (K/9), Walks per 9 Innings (BB/9), Strikeout-to-Walk Ratio (K/BB), Home Runs per 9 Innings (HR/9), batting average against (BAA), and opponent On-Base Percentage (OBP) and On-Base Plus Slugging (OPS). Logistic Regression achieved the highest classification performance with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.804 and classification accuracy of 73.0%, followed by Neural Network (AUC = 0.799, CA = 72.0). Feature importance analysis showed ERA and WHIP, both defense-dependent metrics, as the dominant predictors of postseason qualification, collectively accounting for 33.7% of information gain, while FIP ranks fifth, indicating that defense-dependent metrics are more informative for team success than defense-independent measures. The findings highlight the strategic importance of pitching–defense synergy, demonstrate the applicability of machine learning-based playoff classification beyond Major League Baseball, and provide empirical evidence that defense-dependent metrics (ERA, WHIP) exhibit superior discriminatory power compared to defense-independent metrics (FIP) for team playoff qualification. Findings reflect pitching’s contribution to playoff success; comprehensive models integrating hitting, defense, and managerial factors would provide more complete classification frameworks. Full article