Search Results (1,525)

Efficient and accurate panel assignment is critical in expert and peer review processes. Traditional methods—based on manual preferences or Heuristic rules—often introduce bias, inconsistency, and scalability challenges. We present an automated framework that combines transformer-based document similarity modeling with optimization-based reviewer assignment. Using the all-mpnet-base-v2 from model (version 3.4.1), our system computes semantic similarity between proposal texts and reviewer documents, including CVs and Google Scholar profiles, without requiring manual input from reviewers. These similarity scores are then converted into rankings and integrated into an Integer Linear Programming (ILP) formulation that accounts for workload balance, conflicts of interest, and role-specific reviewer assignments (lead, scribe, reviewer). The method was tested across 40 researchers in two distinct disciplines (Chemical Engineering and Philosophy), each with 10 proposal documents. Results showed high self-similarity scores (0.65–0.89), strong differentiation between unrelated fields (−0.21 to 0.08), and comparable performance between reviewer document types. The optimization consistently prioritized top matches while maintaining feasibility under assignment constraints. By eliminating the need for subjective preferences and leveraging deep semantic analysis, our framework offers a scalable, fair, and efficient alternative to manual or Heuristic assignment processes. This approach can support large-scale review workflows while enhancing transparency and alignment with reviewer expertise. Full article

Polysaccharides and phenols are commonly co-localized in various plant-derived foods, including highland barley (Hordeum vulgare L. var. nudum Hook. f.). The interactions between these compounds can influence multiple characteristics of food products, including their physicochemical properties and functional performance, such as bioavailability, stability, and digestibility, which may support promising application of the phenol and polysaccharide complex in health food industry. In this study, two complexes with potential existence in highland barley, such as β-glucan-ferulic acid (GF) and β-glucan-quercetin (GQ), were prepared using the equilibrium dialysis method in vitro. FTIR and SEM results showed that ferulic acid and quercetin formed complexes with β-glucan separately, with covalent and non-covalent bonds and a dense morphological structure. The pH value, reaction temperature, and concentration of phosphate buffer solution (PBS) were confirmed to have an impact on the formation and yield of the complex. Through the test of the response surface, it was found that the optimum conditions for GF and (GQ) preparations were a pH of 6.5 (6), a PBS buffer concentration of 0.08 mol/L (0.3 mol/L), and a temperature of 8 °C (20 °C). Through in vitro assays, GF and GQ were found to possess good antioxidant activity, with a greater scavenging effect of DPPH, ABTS, and hydroxyl radical than the individual phenolic acids and glucans, as well as their physical mixtures. Taking GF as an example, the DPPH radical scavenging capacity ranked as GF (71.74%) > ferulic acid (49.50%) > PGF (44.43%) > β-glucan (43.84%). Similar trends were observed for ABTS radical scavenging (GF: 54.56%; ferulic acid: 44.37%; PGF: 44.95%; β-glucan: 36.42%) and hydroxyl radical elimination (GF: 39.16%; ferulic acid: 33.06%; PGF: 35.51%; β-glucan: 35.47%). In conclusion, the convenient preparation method and excellent antioxidant effect of the phenol–polysaccharide complexes from highland barley provide new opportunities for industrial-scale production, development, and design of healthy food based on these complexes. Full article

Boda boda motorbike taxis are a primary mode of transport in Nairobi, Kenya, and a major source of urban air pollution. This study investigates the environmental and electrical grid impacts of electrifying Nairobi’s boda boda fleet. Using real-world tracking data from 118 motorbikes, we simulated the effects of a full-scale transition from internal combustion engine (ICE) vehicles to electric motorbikes. We analysed various scenarios, including different battery charging strategies (swapping and home charging), motor efficiencies, battery capacities, charging rates, and the potential for solar power offsetting. The results indicate that electrification could reduce daily ${\mathrm{CO}}_2$ emissions by approximately 85% and eliminate tailpipe particulate matter emissions. However, transitioning the entire country’s fleet would increase the national daily energy demand by up to 6.85 GWh and could introduce peak grid loads as high as 2.40 GW, depending on the charging approach and vehicle efficiency. Battery swapping was found to distribute the grid load more evenly and better complement solar power integration compared to home charging, which concentrates demand in the evening. This research provides a scalable, data-driven framework for policymakers to assess the impacts of transport electrification in similar urban contexts, highlighting the critical trade-offs between environmental benefits and grid infrastructure requirements. Full article

Research in biogeography, ecology, and biodiversity hinges on the availability of comprehensive datasets that detail species distributions and environmental conditions. At the forefront of this endeavor is the Global Biodiversity Information Facility (GBIF). This study focuses on investigating spatial biases and temporal trends in insect pollinator occurrence data within the GBIF dataset, specifically focusing on three pivotal pollinator groups: bees, hoverflies, and butterflies. Addressing these gaps in GBIF data is essential for comprehensive analyses and informed pollinator conservation efforts. We obtained occurrence data from GBIF for seven bee families, six butterfly families, and the Syrphidae family of hoverflies in 2024. Spatial biases were addressed by eliminating duplicate records with identical latitude and longitude coordinates. Species richness was assessed for each family and country. Temporal trends were examined by tallying annual occurrence records for each pollinator family, and the diversity of data sources within GBIF was evaluated by quantifying unique data publishers. We identified initial occurrence counts of 4,922,390 for bees, 1,703,131 for hoverflies, and 31,700,696 for butterflies, with a substantial portion containing duplicate records. On average, 81.4% of bee data, 77.2% of hoverfly data, and 65.4% of butterfly data were removed post-duplicate elimination for dataset refinement. Our dataset encompassed 9286 unique bee species, 2574 hoverfly species, and 17,895 butterfly species. Our temporal analysis revealed a notable trend in data recording, with 80% of bee and butterfly data collected after 2022, and a similar threshold for hoverflies reached after 2023. The United States, Germany, the United Kingdom, and Sweden consistently emerged as the top countries for occurrence data across all three groups. The analysis of data publishers highlighted iNaturalist.org as a top contributor to bee data. Overall, we uncovered significant biases in the occurrence data of pollinators from GBIF. These biases pose substantial challenges for future research on pollinator ecology and biodiversity conservation. Full article

Pathogenic variants in the MAP1B gene have been associated with neurological impairment, including intellectual disability, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, brain malformations, cognitive hearing loss, short stature, and dysmorphic features. However, few cases with detailed clinical characterization have been reported. We describe a 12-year-old boy carrying a loss-of-function MAP1B variant, presenting with severe elimination disorders despite normal intelligence. He was referred to the genetics service due to persistent elimination issues, including daytime urinary incontinence, nocturnal enuresis, and fecal incontinence. He had normal motor and cognitive development, with an IQ of 99; however, he also presented with ADHD, short stature, microcephaly, and myopia. Brain MRI revealed bilaterial subependymal periventricular nodular heterotopia (PVNH). Audiometry showed normal bilateral hearing. Testing fragile X syndrome (FXS) and karyotype analyses yielded normal results. Whole exome sequencing (WES) revealed a nonsense pathogenic variant in MAP1B (c.895 C>T; p.Arg299*). No other family members showed a similar phenotype; however, a great-uncle and a great-aunt had a history of nocturnal enuresis until age 10. The patient’s deceased mother had short stature and psychiatric disorders, and a history of consanguinity was reported on the maternal side. This case broadens the phenotypic spectrum associated with MAP1B syndrome, suggesting that elimination disorder, frequently reported in FXS, should also be evaluated in MAP1B pathogenic variant carriers. In addition, the presence of short stature also appears to be part of the syndrome. Full article

Microalgae proteins are increasingly recognized in the food and nutraceutical industries for their functional versatility and high nutritional value. Mild alkaline treatment is commonly used for cell wall degradation and intracellular protein solubilization, consequently enhancing the protein extraction yield. The findings of this study reveal that alkaline treatment alone, even at higher NaOH concentration (up to 0.3 M) and treatment time (up to 90 min), was ineffective (max. 2.4% yield) for the extraction of protein from Auxenochlorella protothecoides biomass. This challenge was significantly reduced through synergistic application of mechanical cell disruption using high-pressure homogenization (HPH) and alkaline solubilization. Single-pass HPH (35 k psi) alone without alkaline treatment led to 52.3% protein solubilization from wet biomass directly harvested from culture broth, while it was only 18.5% for spray-dried biomass. The combined effect of HPH and alkaline (0.1 M NaOH) treatment significantly increased protein extraction yield to 68.0% for a spray-dried biomass loading of 50 g L⁻¹. Through replacing spray-dried biomass with wet biomass, the requirement of NaOH was reduced by 5-fold to 0.02 M to achieve a similar yield of 68.1%. The process integration of HPH with the mild alkaline solubilization and utilization of wet biomass from culture broth showed high potential for industrialization of microalgae protein extraction. This method achieves high extraction yield while reducing alkaline waste and eliminating the need for energy-consuming drying of biomass, thereby minimizing the environmental impact. Full article

Background/Objectives: Hodgkin’s lymphoma (HL) affects 2–4 individuals per 100,000 annually. Standard treatment includes radiotherapy and ABVD chemotherapy, achieving a 95% survival rate. However, HL survivors face an elevated risk of treatment-related morbidity, particularly the development of secondary malignancies. Previous studies have demonstrated that ABVD treatment induces a high frequency of chromosomal aberrations (CAs) in lymphocytes from HL patients, with higher frequencies one year after treatment than during treatment. This study aimed to determine whether HL treatment also induces unclassified chromosomal/nuclear aberrations (UnCAs) in the lymphocytes of HL patients, and whether these alterations may serve as complementary indicators of genomic instability. Methods: Peripheral blood lymphocytes from HL patients were collected at three time points: before treatment (BT), during treatment (DT), and one year after treatment (1yAT) with ABVD chemotherapy and radiotherapy. A minimum of 3000 nuclei were analyzed per patient to identify and quantify UnCAs. These results were compared to UnCA frequencies in healthy individuals. Results: The percentage of cells presenting UnCAs per 3000 nuclei was 23.92% BT, 18.58% DT, and 30.62% 1yAT. All values were significantly higher (p < 0.016) than the 8.16% observed in healthy controls. The increase was primarily driven by free chromatin and micronuclei clusters. UnCA frequency was lower during treatment than one year after, likely due to the elimination of highly damaged cells through apoptosis or lack of proliferative capacity. Over time, however, persistent genomic damage appears to accumulate in surviving cells, becoming more evident post-treatment. A parallel trend was observed between the frequencies of UnCAs free chromatin, micronucleus and micronuclei clusters, and classical CAs, showing a similar pattern of genomic damage induced by therapy. Conclusions: The post-treatment increase in UnCAs indicates ongoing genomic instability, possibly driven by the selective survival of hematopoietic stem cells with higher genomic fitness. Given their persistence and association with therapy-induced damage, free chromatin and micronuclei clusters may serve as early biomarkers for secondary cancer risk in HL survivors. Full article

We propose an efficient Nyström-based unitary subspace method for low-complexity two-dimensional (2D) direction-of-arrival (DOA) estimation in uniform rectangular array (URA) signal processing systems. The conventional high-resolution DOA estimation methods often suffer from excessive computational complexity, particularly when dealing with large-scale antenna arrays. The proposed method addresses this challenge by combining the Nyström approximation with a unitary transformation to reduce the computational burden while maintaining estimation accuracy. The signal subspace is approximated using a partitioned covariance matrix, and a real-valued transformation is applied to further simplify the eigenvalue decomposition (EVD) process. Furthermore, the linear prediction coefficients are estimated via a weighted least squares (WLS) approach, enabling robust extraction of the angular parameters. The 2D DOA estimates are then derived from these coefficients through a closed-form solution, eliminating the need for exhaustive spectral searches. Numerical simulations demonstrate that the proposed method achieves comparable estimation performance to state-of-the-art techniques while significantly reducing computational complexity. For a fixed array size of $M=N=20$, the proposed method demonstrates significant computational efficiency, requiring less than $50\%$ of the running time compared to conventional ESPRIT, and only $6\%$ of the time required by ML methods, while maintaining similar performance. This makes it particularly suitable for real-time applications where computational efficiency is critical. The novelty lies in the integration of Nyström approximation and unitary subspace techniques, which jointly enable efficient and accurate 2D DOA estimation without sacrificing robustness against noise. The method is applicable to a wide range of array processing scenarios, including radar, sonar, and wireless communications. Full article

To circumvent the computational bottlenecks associated with the intermediate steps (e.g., least squares fitting) in conventional sine wave similarity principles and directly acquire the energy metrics required for stabilized sinusoidal waveform characterization, this study leverages time domain probability distribution theory. From a complementary advantage perspective, a novel transformer inrush current identification criterion is developed using the Wasserstein distance metric. The methodology employs feature discretization to extract target/template signals, transforming them into state vectors for sample labelling. By quantifying inter-signal energy distribution disparities through this framework, it achieves a precise waveform similarity assessment in sinusoidal regimes. The theoretical analysis and simulations demonstrate that the approach eliminates frequency domain computations while maintaining implementation simplicity. Compared with conventional sine wave similarity methods, the solution streamlines protection logic and significantly enhances practical applicability with accelerated response times. Furthermore, tests conducted on field-recorded circuit breaker closing waveforms using MATLAB R2022a confirm the effectiveness of the proposed method in improving transformer protection performance. Full article

Buildings are a significant component of urban space and are essential to smart cities, catastrophe monitoring, and land use planning. However, precisely extracting building polygons from remote sensing images remains difficult because of the variety of building designs and intricate backgrounds. This paper proposes an end-to-end polygon dynamic adjustment algorithm (PDAA) to improve the accuracy and geometric consistency of building contour extraction by dynamically generating and optimizing polygon vertices. The method first locates building instances through the region of interest (RoI) to generate initial polygons, and then uses four core modules for collaborative optimization: (1) the feature enhancement module captures local detail features to improve the robustness of vertex positioning; (2) the contour vertex tuning module fine-tunes vertex coordinates through displacement prediction to enhance geometric accuracy; (3) the learnable redundant vertex removal module screens key vertices based on a classification mechanism to eliminate redundancy; and (4) the missing vertex completion module iteratively restores missed vertices to ensure the integrity of complex contours. PDAA dynamically adjusts the number of vertices to adapt to the geometric characteristics of different buildings, while simplifying the prediction process and reducing computational complexity. Experiments on public datasets such as WHU, Vaihingen, and Inria show that PDAA significantly outperforms existing methods in terms of average precision (AP) and polygon similarity (PolySim). It is at least 2% higher than existing methods in terms of average precision (AP), and the generated polygonal contours are closer to the real building geometry. Values of 75.4% AP and 84.9% PolySim were achieved on the WHU dataset, effectively solving the problems of redundant vertices and contour smoothing, and providing high-precision building vector data support for scenarios such as smart cities and emergency response. Full article

Formation MicroScanner Image (FMI) technology is a key method for identifying fractured reservoirs and optimizing oil and gas exploration, but its inherent insufficient resolution severely constrains the fine characterization of geological features. This study innovatively applies a Super-Resolution Generative Adversarial Network (SRGAN) to the super-resolution reconstruction of FMI logging image to address this bottleneck problem. By collecting FMI logging image of glutenite from a well in Xinjiang, a training set containing 24,275 images was constructed, and preprocessing strategies such as grayscale conversion and binarization were employed to optimize input features. Leveraging SRGAN’s generator-discriminator adversarial mechanism and perceptual loss function, high-quality mapping from low-resolution FMI logging image to high-resolution images was achieved. This study yields significant results: in RGB image reconstruction, SRGAN achieved a Peak Signal-to-Noise Ratio (PSNR) of 41.39 dB, surpassing the optimal traditional method (bicubic interpolation) by 61.6%; its Structural Similarity Index (SSIM) reached 0.992, representing a 34.1% improvement; in grayscale image processing, SRGAN effectively eliminated edge blurring, with the PSNR (40.15 dB) and SSIM (0.990) exceeding the suboptimal method (bilinear interpolation) by 36.6% and 9.9%, respectively. These results fully confirm that SRGAN can significantly restore edge contours and structural details in FMI logging image, with performance far exceeding traditional interpolation methods. This study not only systematically verifies, for the first time, SRGAN’s exceptional capability in enhancing FMI resolution, but also provides a high-precision data foundation for reservoir parameter inversion and geological modeling, holding significant application value for advancing the intelligent exploration of complex hydrocarbon reservoirs. Full article

Accurate weed detection in soybean fields is essential for enhancing crop yield and reducing herbicide usage. This study proposes a YOLO-SW model, an improved version of YOLOv8, to address the challenges of detecting weeds that are highly similar to the background in natural environments. The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. Additionally, a dataset of six common soybean weeds was expanded to 12,500 images through simulated fog, rain, and snow augmentation, effectively resolving data imbalance and boosting model robustness. The experimental results highlight both the technical superiority and practical relevance: YOLO-SW achieves 92.3% mAP@50 (3.8% higher than YOLOv8), with recognition accuracy and recall improvements of 4.2% and 3.9% respectively. Critically, on the NVIDIA Jetson AGX Orin platform, it delivers a real-time inference speed of 59 FPS, making it suitable for seamless deployment on intelligent weeding robots. This low-power, high-precision solution not only bridges the gap between deep learning and precision agriculture but also enables targeted herbicide application, directly contributing to sustainable farming practices and environmental protection. Full article

Infectious diseases can spread through virus-laden respiratory droplets exhaled into the air. Ventilation systems are crucial in indoor settings as they can dilute or eliminate these droplets, underscoring the importance of understanding their efficacy in the management of indoor infections. Within the field of fluid dynamics methods, the dispersed droplets may be approached through either a Lagrangian framework or an Eulerian framework. In this study, various Eulerian methodologies are systematically compared against the Eulerian–Lagrangian (E-L) approach across three different scenarios: the pseudo-single-phase model (PSPM) for assessing the transport of gaseous pollutants in an office with displacement ventilation (DV), stratum ventilation (SV), and mixing ventilation (MV); the two-fluid model (TFM) for evaluating the transport of non-evaporating particles within an office with DV and MV; and the two-fluid model-population balance equation (TFM-PBE) approach for analyzing the transport of evaporating droplets in a ward with MV. The Eulerian and Lagrangian approaches present similar agreement with the experimental data, indicating that the two approaches are comparable in accuracy. The computational cost of the E-L approach is closely related to the number of tracked droplets; therefore, the Eulerian approach is recommended when the number of droplets required by the simulation is large. Finally, the performances of DV, SV, and MV are presented and discussed. DV creates a stratified environment due to buoyant flows, which transport respiratory droplets upward. MV provides a well-mixed environment, resulting in a uniform dispersion of droplets. SV supplies fresh air directly to the breathing zone, thereby effectively reducing infection risk. Consequently, DV and SV are preferred to reduce indoor infection. Full article

Background: Treatment of metastatic cancer remains a challenge, because cancer cells acquire resistance even to the most contemporary therapies. This study analyzed the role of the phosphoprotein Stathmin 1 (STMN1) in regulating cancer cell growth and metastatic potential. Methods: Public datasets with metastatic castration-resistant prostate cancer (mCRPC) and breast cancer (BC) were analyzed to determine the interrelationship between STMN1, hepatocyte growth factor (HGF) and MET proto-oncogene (MET) expression, overall survival, and response to chemotherapy. Site-directed mutagenesis, cell cycle analysis, proliferation, and migration and invasion assays determined the impact of STMN1 phosphorylation on proliferation and metastatic potential. Results: Increased STMN1 associates with HGF and MET gene expression in mCRPC, and taxane chemotherapy further increases HGF expression. STMN1 and HGF are highest, and overall survival is poorest in mCRPC in the liver compared to other sites, implying the metastatic site influences their expression levels and potentially the pattern of metastatic spread. Increased STMN1 and MET also predict taxane responsiveness in BC patients. Analysis of STMN1 serine (S)16, 25, 38, and 63 determined that total (t) STMN1 and STMN1 S16 phosphorylation (pSTMN1^S16) are co-regulated by HGF/MET during cell cycle progression, pSTMN1^S16 alone can promote cell proliferation, and pSTMN1^S16 shortens the cell cycle similar to HGF treatment, while STMN1^S16 dephosphorylation lengthens the cell cycle to arrest cell growth in G2/M, similar to HGF plus the MET inhibitor AMG337. Importantly, STMN1^S16 does not promote metastasis. Conclusions: Selectively inhibiting STMN1^S16 phosphorylation may provide an alternative strategy for inhibiting MET-mediated cell growth to eliminate metastatic cancer cells and inhibit further metastasis. Full article

The paper introduces EVOCA—Explainable Verification Of Claims by Graph Alignment—a hybrid approach that combines NLP (Natural Language Processing) techniques with the structural advantages of knowledge graphs to manage and reduce the amount of evidence required to evaluate statements. The approach leverages the explicit and interpretable structure of semantic graphs, which naturally represent the semantic structure of a sentence—or a set of sentences—and explicitly encodes the relationships among different concepts, thereby facilitating the extraction and manipulation of relevant information. The primary objective of the proposed tool is to condense the evidence into a short sentence that preserves only the salient and relevant information of the target claim. This process eliminates superfluous and redundant information, which could impact the performance of the subsequent verification task and provide useful information to explain the outcome. To achieve this, the proposed tool called EVOCA—Explainable Verification Of Claims by Graph Alignment—generates a sub-graph in AMR (Abstract Meaning Representation), representing the tokens of the claim–evidence pair that exhibit high semantic similarity. The structured representation offered by the AMR graph not only aids in identifying the most relevant information but also improves the interpretability of the results. The resulting sub-graph is converted back into natural language with the SPRING AMR tool, producing a concise but meaning-rich “sub-evidence” sentence. The output can be processed by lightweight language models to determine whether the evidence supports, contradicts, or is neutral about the claim. The approach is tested on the 4297 sentence pairs of the Climate-BERT-fact-checking dataset, and the promising results are discussed. Full article