Search Results (20,209)

Device-free localization and tracking (DFLT) has emerged as a promising technique for location-aware Internet-of-Things (IoT) applications. However, most existing DFLT systems based on narrowband sensing networks suffer from reduced accuracy in indoor environments due to the susceptibility of received signal strength (RSS) measurements to multipath interference. In this paper, we propose a real-time DFLT system leveraging ultra-wideband (UWB) sensors. The system estimates target-induced shadowing using two UWB RSS measurements, which are shown to be more resilient to multipath effects compared to their narrowband counterparts. To enable real-time tracking, we further design an efficient measurement protocol tailored for UWB networks. Field experiments conducted in both indoor and outdoor environments demonstrate that our UWB-based system significantly outperforms its traditional narrowband DFLT solutions in terms of accuracy and robustness. Full article

Multiple markers exist for breast cancer stem cells (CSCs), which are believed to represent the phenotypes of various CSC types and/or states. The relationship between each CSC subpopulation/state and the primary hallmarks of cancer has not been sufficiently clarified. In this study, six CSC markers (CD44^hi/CD24^lo, CD24, Ep-CAM, ALDH1, CD10, and BMI1) were assessed in a surgical cohort of 73 breast cancer patients. The expression of a single or multiple CSC markers was correlated with clinicopathological parameters, including markers of immune evasion, proliferation, epithelial–mesenchymal transition (EMT), and survival. All CSC phenotypes, except for CD10, correlated with markers indicative of higher proliferation. The CD44^hi/CD24^lo phenotype correlated with markers of EMT and PD-L1 expression, unlike ALDH1^hi. Both Ep-CAM^hi and CD24^hi breast cancer were associated with indicators of immune evasion, including PD-L1 expression, and the infiltration of FOXP3+ and PD-1+ tumor-infiltrating lymphocytes (TIL). While the CD44^hi/CD24^lo, Ep-CAM^hi, and ALDH1^hi phenotypes correlated with shorter overall survival (OS), CD24^hi correlated with reduced disease-free survival (DFS). Interestingly, among all tested CSC markers, the CD44^hi/CD24^lo-ALDH1^hi combination phenotype correlated with the worst DFS (HR 2.8, p = 0.014 in univariate/multivariate analysis) and OS (p < 0.001, HR 6.4 in univariate and 5.4 in multivariate analysis). A side-by-side comparison of multiple CSC markers demonstrated the differential linkage of CSC phenotype/state with distinct features of breast cancer. This comparison demonstrates the advantage of the CD44^hi/CD24^lo-ALDH1^hi combination marker for prognostication, especially after neoadjuvant chemotherapy. In the future, distinct markers of CSCs can hopefully be leveraged to trace/monitor different disease characteristics or treatment outcomes. Full article

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article

We address the task of point cloud denoising by leveraging a diffusion-based generative framework augmented with adversarial training. While recent diffusion models have demonstrated strong capabilities in learning complex data distributions, their effectiveness in recovering fine geometric details remains limited, especially under severe noise conditions. To mitigate this, we propose the Adversarial Diffusion Bridge Model (ADBM), a novel approach for denoising 3D point cloud data by integrating a diffusion bridge model with adversarial learning. ADBM incorporates a lightweight discriminator that guides the denoising process through adversarial supervision, encouraging sharper and more faithful reconstructions. The denoiser is trained using a denoising diffusion objective based on a Schrödinger Bridge, while the discriminator distinguishes between real, clean point clouds and generated outputs, promoting perceptual realism. Experiments are conducted on the PU-Net and PC-Net datasets, with performance evaluation employing the Chamfer distance and Point-to-Mesh metrics. The qualitative and quantitative results both highlight the effectiveness of adversarial supervision in enhancing local detail reconstruction, making our approach a promising direction for robust point cloud restoration. Full article

Automatic skin lesion segmentation is essential for early melanoma diagnosis, yet the scarcity and limited diversity of annotated training data hinder progress. We introduce a two-stage framework that first employs a denoising diffusion probabilistic model (DDPM) enhanced with dilated convolutions and self-attention to synthesize unseen, high-fidelity dermoscopic images. In the second stage, segmentation models—including a dilated U-Net variant that leverages dilated convolutions to enlarge the receptive field—are trained on the augmented dataset. Experimental results demonstrate that this approach not only enhances segmentation accuracy across various architectures with an increase in DICE of more than 0.4, but also enables compact and computationally efficient segmentation models to achieve performance comparable to or even better than that of models with 10 times the parameters. Moreover, our diffusion-based data augmentation strategy consistently improves segmentation performance across multiple architectures, validating its effectiveness for developing accurate and deployable clinical tools. Full article

Plant disease segmentation has achieved significant progress with the help of artificial intelligence. However, deploying high-accuracy segmentation models in resource-limited settings faces three key challenges, as follows: (A) Traditional dense attention mechanisms incur quadratic computational complexity growth (O($n^{2}d$)), rendering them ill-suited for low-power hardware. (B) Naturally sparse spatial distributions and large-scale variations in the lesions on leaves necessitate models that concurrently capture long-range dependencies and local details. (C) Complex backgrounds and variable lighting in field images often induce segmentation errors. To address these challenges, we propose Sparse-MoE-SAM, an efficient framework based on an enhanced Segment Anything Model (SAM). This deep learning framework integrates sparse attention mechanisms with a two-stage mixture of experts (MoE) decoder. The sparse attention dynamically activates key channels aligned with lesion sparsity patterns, reducing self-attention complexity while preserving long-range context. Stage 1 of the MoE decoder performs coarse-grained boundary localization; Stage 2 achieves fine-grained segmentation by leveraging specialized experts within the MoE, significantly enhancing edge discrimination accuracy. The expert repository—comprising standard convolutions, dilated convolutions, and depthwise separable convolutions—dynamically routes features through optimized processing paths based on input texture and lesion morphology. This enables robust segmentation across diverse leaf textures and plant developmental stages. Further, we design a sparse attention-enhanced Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contexts for both extensive lesions and small spots. Evaluations on three heterogeneous datasets (PlantVillage Extended, CVPPP, and our self-collected field images) show that Sparse-MoE-SAM achieves a mean Intersection-over-Union (mIoU) of 94.2%—surpassing standard SAM by 2.5 percentage points—while reducing computational costs by 23.7% compared to the original SAM baseline. The model also demonstrates balanced performance across disease classes and enhanced hardware compatibility. Our work validates that integrating sparse attention with MoE mechanisms sustains accuracy while drastically lowering computational demands, enabling the scalable deployment of plant disease segmentation models on mobile and edge devices. Full article

Weeds are a significant factor affecting crop yield and quality. Herbicides have made crucial contributions to ensuring stable and high grain production, but the low effective utilization rate and short duration of traditional formulations have led to excessive application and a range of ecological and environmental issues. Nanoherbicides, particularly carrier-coated systems, can simultaneously leverage the small size, large specific surface area, and high permeability of nanoparticles, as well as the multifunctionality of carriers, to synergistically enhance the efficacy and safety of the formulations. This provides a scientific and promising strategy for overcoming the functional deficiencies of traditional formulations. Nevertheless, there are currently relatively few articles that systematically review the research progress and performance advantages of nanoherbicides. This review provides a concise overview of the preparation methods and structural characteristics of nanoherbicides. It primarily highlights the classification of carrier-coated nanoherbicides, along with representative studies and their distinctive properties across various categories. Based on this foundation, the performance advantages of nanoherbicides are systematically summarized. Finally, the major challenges and future prospects in this research field are proposed. This review offers valuable insights and methodological guidance for the design and rational application of efficient, environmentally friendly nanoherbicides. Full article

Sea breezes alleviate coastal heat stress via cooling and humidifying. Sendai, Japan, in 2015 had a population of 1.08 million and an area of 786 km². Integrating the WRF model with RayMan, this study employs the PET index to assess spatiotemporal distributions of thermal comfort and heat stress, and their influencing factors, on typical summer sea-breeze days in Sendai, Japan. Results indicate that in the coastal zone, PET was primarily regulated by air temperature (Ta) and relative humidity (RH). In contrast, wind speed was the dominant influence on urban/inland zones, with Ta and RH contributing more during the evening. Sea breezes markedly improved the thermal environment in the coastal zone, suppressing PET increases. PET in urban and inland zones exhibited an initial rise followed by a decline, with the inland zone experiencing sustained extreme heat stress for 3 h. Among regions experiencing extreme heat stress, inland zones showed the highest proportion (17.75%), while coastal zones had the lowest (2.14%). Proportions across the three zones were similar under nighttime conditions with no thermal stress, with the urban zone exhibiting a slightly lower proportion. This study provides a theoretical basis for climate-adaptive urban planning leveraging sea breezes as a resource. Full article

During the last decades, development of novel low-cost sensors commercialized for indoor air quality measurements has gained interest. In this research, three AirVisual Pro air quality monitors were used to monitor PM_2.5 and carbon dioxide concentrations in which two were installed indoors and one outdoors at two residential apartments in Central Europe (Budapest, Hungary). In our research, we present a methodology to support the evaluation of indoor sensors by utilizing official outdoor monitoring data, leveraging the fact that indoor spaces are frequently ventilated and thus influenced by outdoor conditions. We compared six-year measurement data (01.2017–12.2022) with outdoor concentrations provided by the Hungarian Air Quality Monitoring Network (HAQM). However, the well-known low spatial representativeness and high spatio-temporal variability of PM_2.5 in city environments made this evaluation problematic, which needed to be addressed before comparison. Here we quantify the spatial heterogeneity of the HAQM PM_2.5 data for a maximum of eight stations. Then, based on the carbon dioxide readings of the AirVisual Pro units, data filtering was performed for the AirVisual 1 and AirVisual 2 sensors located in indoor environments to identify ventilated periods (nearly 10,000 ventilated events) for the AirVisual 1 and AirVisual 2 sensors, respectively, for the comparison of indoor and outdoor PM_2.5 concentrations. The AirVisual 3 sensor was placed in a garden storage, and the measurements taken there were considered outdoor values throughout. Finally, four heterogeneity criteria were set for the HAQM data to filter conditions that were assumed to be comparable with the indoor sensor data. The results indicate that the spatial heterogeneity was indeed detectable, and in approximately 50–60% of the cases, the readings could be considered as non-representative to single location comparison, but the results depend on the selected homogeneity criteria. The AirVisual and HAQM comparison indicated relatively low sensitivity to heterogeneity criteria, which is a promising result that can be exploited. AirVisual sensors generally overestimated PM_2.5, but this bias could be corrected with a simple linear adjustment. Slopes changed across sensors (0.83–0.85 for AirVisual 1, 0.48–0.53 for AirVisual 2, and 0.70–0.73 for AirVisual 3), indicating general overestimation and correlations from moderate to high (R² = 0.45–0.89) depending on the device. In contrast, when we compared the measurements only with data from the nearest reference station, we obtained a weaker match and slopes that did not match those calculated by taking into account homogeneity criteria. This research contributes to the proliferation of citizen science and supports the application of LCSs in indoor conditions. Full article

Medicinal plant species are crucial biological resources, and yet their conservation in the Kingdom of Saudi Arabia remains insufficiently studied. This study conducts a comprehensive gap analysis of 74 priority medicinal plant species in the Kingdom of Saudi Arabia to assess their spatial distribution, identify conservation gaps and propose strategic recommendations. Occurrence records were collected from field surveys and global biodiversity databases, followed by ecogeographical land characterization and conservation gap analyses using the CAPFITOGEN3 tools. The results reveal significant disparities in in situ and ex situ conservation efforts, with two biodiversity hotspots, Asir and Jazan, containing the highest species diversity. While 66 species occur within protected areas, seven species are currently only recorded outside protected areas, indicating opportunities for expanding conservation efforts. Complementarity analysis identified 13 optimal protected areas for priority medicinal plants’ conservation, alongside 20 potential sites outside protected areas that could serve as other effective area-based conservation measures. Ex situ conservation remains critically limited for many species, with only 10 represented in genebanks and all accessions currently stored internationally, although some medicinal plant species may have broader global distributions. To bring about improved outcomes of conservation, the expansion of in situ conservation coverage, integration of other effective area-based conservation measures, strengthening of national genebanks and leverage of biotechnology and geospatial tools is recommended by this study. The findings of this study can be used to develop a more systematic and sustainable approach to the conservation of medicinal plants in the Kingdom of Saudi Arabia. Full article

Manual tissue documentation is a critical step in the field of pathology that sets the stage for microscopic analysis and significantly influences diagnostic outcomes. In routine practice, technicians verbally dictate descriptions of specimens during gross examination; these are later transcribed into macroscopic reports. Fragment sizes are measured manually with rulers; however, these measurements are often inconsistent for small, irregular biopsies. No photographic record is captured for traceability. To address these limitations, we propose a proof-of-concept framework that automates the image capture and documentation of biopsy and resection cassettes. It integrates a custom imaging platform and a segmentation pipeline leveraging the YOLOv8 and YOLOv9 architectures to improve accuracy and efficiency. The framework was tested in a real clinical context and was evaluated on two datasets of 100 annotated images each, achieving a mask mean Average Precision (mAP) of 0.9517 ± 0107 and a tissue fragment spatial accuracy of 96.20 ± 1.37%. These results demonstrate the potential of our framework to enhance the standardization, reliability, and speed of macroscopic documentation, contributing to improved traceability and diagnostic precision. Full article

The rapid growth of e-commerce has created substantial environmental impacts, driving the need for advanced optimization models to enhance supply chain sustainability. As consumer preferences shift toward environmental responsibility, organizations must adopt robust quantitative methods to reduce ecological footprints while ensuring operational efficiency. This study develops a novel hybrid multi-criteria decision-making (MCDM) model to evaluate and prioritize green suppliers under uncertainty, integrating the rough-Dombi best–worst method (BWM) and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed model addresses two key challenges: (1) inconsistency in expert judgments through rough set theory and Dombi aggregation operators and (2) ranking instability via an enhanced TOPSIS formulation that mitigates rank reversal. Mathematically, the rough-Dombi BWM leverages interval-valued rough numbers to model subjective expert preferences, while the Dombi operator ensures flexible and precise weight aggregation. The modified TOPSIS incorporates a dynamic distance metric to strengthen ranking robustness. A case study of five e-commerce suppliers validates the model’s effectiveness, with results identifying cost, green competitiveness, and external environmental management as the dominant evaluation dimensions. Key indicators—such as product price, pollution control, and green design—are rigorously prioritized using the proposed framework. Theoretical contributions include (1) a new rough-Dombi fusion for criteria weighting under uncertainty and (2) a stabilized TOPSIS variant with reduced sensitivity to data perturbations. Practically, the model provides e-commerce enterprises with a computationally efficient tool for sustainable supplier selection, enhancing resource allocation and green innovation. This study advances the intersection of uncertainty modeling, operational research, and sustainability analytics, offering scalable methodologies for mathematical decision-making in supply chain contexts. Full article

Background: A short cervix in the second trimester significantly increases preterm birth risk, yet no reliable first-trimester prediction method exists. Current guidelines lack consensus on which women should undergo transvaginal ultrasound (TVUS) screening for cost-effective prevention. Therefore, it is vital to establish a highly accurate and economical method for use in the early stages of pregnancy to predict short cervix in mid-pregnancy. Methods: A total of 1480 pregnant women with singleton pregnancies and at least one risk factor for spontaneous preterm birth (<37 weeks) were recruited from January 2020 to December 2020 at the Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine. Cervical length was assessed at 20–24 weeks of gestation, with a short cervix defined as <25 mm. Feature selection employed tree models, regularization, and recursive feature elimination (RFE). Seven machine learning models (logistic regression, linear discriminant analysis, k-nearest neighbors, support vector machine, decision tree, random forest, XGBoost) were trained to predict mid-trimester short cervix. The XGBoost model—an ensemble method leveraging sequential decision trees—was analyzed using Shapley Additive Explanation (SHAP) values to assess feature importance, revealing consistent associations between clinical predictors and outcomes that align with known clinical patterns. Results: Among 1480 participants, 376 (25.4%) developed mid-trimester short cervix. The XGBoost-based prediction model demonstrated high predictive performance in the training set (Recall = 0.838, F1 score = 0.848), test set (Recall = 0.850, F1 score = 0.910), and an independent dataset collected in January 2025 (Recall = 0.708, F1 score = 0.791), with SHAP analysis revealing pre-pregnancy BMI as the strongest predictor, followed by second-trimester pregnancy loss history, peripheral blood leukocyte count (WBC), and positive vaginal microbiological culture results (≥10⁵ CFU/mL, measured between 11⁺⁰ and 13⁺⁶ weeks). Conclusions: The XGBoost model accurately predicts mid-trimester short cervix using first-trimester clinical data, providing a 6-week window for targeted interventions before the 20–24-week gestational assessment. This early prediction could help guide timely preventive measures, potentially reducing the risk of spontaneous preterm birth (sPTB). Full article

Optical wireless sensor networks (OWSNs) offer promising capabilities for high-speed, energy-efficient communication, particularly in mission-critical environments such as industrial automation, healthcare monitoring, and smart buildings. However, dynamic spectrum management and fault tolerance remain key challenges in ensuring reliable and timely data transmission. This paper proposes an adaptive spectrum management framework (ASMF) that addresses these challenges through a mathematically grounded and implementation-driven approach. The ASMF formulates the spectrum allocation problem as a constrained Markov decision process and leverages a dual-layer optimization strategy combining Lyapunov drift-plus-penalty for queue stability with deep reinforcement learning for adaptive long-term decision making. Additionally, ASMF integrates a hybrid fault-tolerant mechanism using LSTM-based link failure prediction and lightweight recovery logic, achieving up to 83% prediction accuracy. Experimental evaluations using real-world datasets from industrial, healthcare, and smart infrastructure scenarios demonstrate that ASMF reduces critical traffic latency by 37%, improves reliability by 42% under fault conditions, and enhances energy efficiency by 22.6% compared with state-of-the-art methods. The system also maintains a 99.94% packet delivery ratio for critical traffic and achieves 69.7% faster recovery after link failures. These results confirm the effectiveness of ASMF as a robust and scalable solution for adaptive spectrum management in dynamic, fault-prone OWSN environments. Full article

This study develops a four-dimensional value-assessment framework encompassing economic, innovation, social, and cultural dimensions to evaluate the multidimensional performance of family firms in China. Drawing on the entropy weighting method, we construct a composite value index for 251 A-share listed family firms from 2014 to 2023 and apply spatial statistical techniques—including Dagum Gini coefficients, Theil indices, and coefficients of variation—to examine temporal evolution and regional disparities. We further estimate explanatory panel models with firm and year fixed effects (Hausman test favoring FE) to identify the firm-level determinants of composite value. Leverage exhibits a significantly negative association with value, while firm size and innovation capacity are positively related; no significant moderating effect of technology-intensive industry is found. A robustness check using equal weights (0.25 for each dimension) yields an almost perfect correlation (0.9999) with the entropy-weighted index, confirming that the dominance of the innovation dimension in the weighting scheme does not materially affect the overall conclusions. The results highlight the importance of integrating multidimensional value perspectives into both academic research and policy design to promote balanced, inclusive, and sustainable development trajectories for family enterprises. Full article