Search Results (62,337)

Employing a 4 (opponent type) × 2 (communication condition) between-subjects design, the study recruited 194 valid human participants to complete three rounds of game tasks. Results revealed: (1) The type of game counterpart exerted a significant main effect on participants’ remaining funds (F(3, 185) = 3.179, p = 0.025). Human participants retained significantly more funds when the counterpart was a real large model compared to other groups. (2) A significant interaction existed between the type of game counterpart and communication conditions (F(3, 185) = 3.318, p = 0.021). Specifically, when the opponent was a fake AI model (presented as human but actually an AI), human participants’ remaining funds were significantly higher under the communication condition than without communication (p = 0.012). This indicates that communication can promote rational decision-making in identity mismatch scenarios by providing additional behavioral cues. In the fake-human group (informed as human but actually AI), a numerical trend toward increased funds was also observed under communication conditions, though it did not reach statistical significance (p = 0.159); (3) The moderating effect of social value orientation did not reach significance. These findings extend the application of the theory of mind in human–machine games, revealing the complex influence mechanism of identity perception and communication dynamics on rational decision-making. Full article

Excessive acidity restricts the utilization of citrus pulp, a major by-product of the dried tangerine peel industry. To overcome this bottleneck, a functional microbial consortium (BsHpMrF) comprising Bacillus subtilis L4, Hanseniaspora pseudoguilliermondii B4, and Monascus ruber CGMCC 10910 was constructed for efficient biological deacidification. The consortium exhibited a synergistic effect, achieving an 88.23% reduction in total acidity and converting the acidic pulp into a neutral, bio-stabilized substrate. Untargeted metabolomics analysis revealed that this efficiency was driven by the concurrent activation of the TCA cycle and glyoxylate shunt for organic acid mineralization, coupled with membrane lipid remodeling (increased unsaturation) to enhance acid tolerance. Notably, the fermentation process functioned as a “metabolic factory”, significantly enriching the matrix with bioactive lipids (e.g., 10-HDA, nervonic acid) and indole-3-acetic acid (IAA, 414.28 mg/L). Application assays demonstrated that the fermentation products acted as a potent biostimulant for soybean sprouts, significantly promoting lateral roots and eliciting the accumulation of antioxidant phenolics and flavonoids. This study provides a sustainable “waste-to-treasure” strategy, valorizing acidic citrus pulp into a functional biostimulant for high-quality edible sprout production, thereby achieving a sustainable “waste-to-food” circular loop. Full article

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article

Introduction: The majority of progressive multifocal leukoencephalopathy (PML) cases is still represented by patients affected by acquired immunodeficiency syndrome (AIDS). Diagnosis of PML relies on histopathological findings or by the combination of clinical signs, radiological evidence, and molecular positivity of the JC virus in cerebrospinal fluid. However, AIDS status predisposes to various diseases involving the brain, testing the diagnostic ability of the clinician. Case description: We describe a PML case in a patient with AIDS, in whom lumbar puncture was initially impossible for severe thrombocytopenia and magnetic resonance showed an hyperintense lesion and was unable to distinguish between PML and lymphoma. In this case, [¹⁸F]-fluorodeoxyglucose (FDG)-PET imaging showing a hypometabolism of the lesion helped to initially orient toward PML, as diagnosis was later confirmed by lumbar puncture. We collected 21 cases in the literature in which [¹⁸F]-FDG-PET was helpful in cases of PML. Discussion and Conclusions: PET imaging is not considered a standard diagnostic tool for PML. However, in selected cases, it may provide valuable information to direct the diagnosis towards PML. Full article

The theory of physical degrees of freedom (DoF) developed by Franceschetti–Migliore–Minero (FMM) establishes a fundamental phase transition in the singular-value spectrum of electromagnetic radiation operators under maximal rotational symmetry. In this work, we revisit this result from a symmetry-explicit operator-theoretic perspective and extend it to scenarios with reduced and controllable symmetries, with particular emphasis on reconfigurable intelligent surfaces (RISs). We model the radiation process as a compact operator acting between admissible source and observation spaces and characterize its symmetry through group equivariance. This formulation enables a systematic decomposition of the operator into irreducible representation sectors associated with the effective symmetry group, defined as the intersection of symmetries supported jointly by the source architecture, RIS geometry and programmability, receiver configuration, and propagation environment. We show that the FMM phase transition persists within each symmetry sector and that the total DoF budget is redistributed across sectors according to symmetry constraints. A key outcome of this analysis is the distinction between physical and effective degrees of freedom. While breaking the maximal $\mathrm{SO}\left(2\right)$ symmetry does not increase the total number of electromagnetic DoF dictated by physics, symmetry reduction modifies their allocation across sectors, potentially lifting degeneracies and increasing the number of degrees of freedom that can be effectively addressed by a given excitation, RIS control, and measurement architecture, even when the total number of physical DoF remains fixed by fundamental limits. This clarifies the role of controlled symmetry breaking as a design mechanism rather than a means to surpass fundamental limits. The proposed framework bridges electromagnetic operator theory, representation theory, and RIS-enabled system design, providing both rigorous symmetry-resolved DoF accounting and actionable insights for excitation, surface programmability, and measurement strategies under practical architectural constraints. Full article

In the context of the rapid development of smart horticulture, a deep remote sensing-based dual detection method for horticultural crop growth anomalies and safety risks was proposed to address the limitations of existing remote sensing monitoring approaches. These conventional methods, which predominantly focused on growth vigor assessment or single-task anomaly detection, had difficulty distinguishing anomalies from actual production risks and exhibited insufficient sensitivity to weak anomalies and complex temporal disturbances. Within a unified framework, a growth state modeling branch and an anomaly perception branch were constructed, enabling the joint modeling of normal growth trajectories and anomalous deviation features. By further introducing a risk joint discrimination mechanism, an integrated analysis pipeline from anomaly identification to risk assessment was achieved. Multi-temporal remote sensing features were used as inputs, through which normal crop growth patterns were characterized via trend perception, texture modeling, and temporal aggregation, while sensitivity to local disturbances and weak anomaly signals was enhanced by anomaly embeddings and energy representations. Systematic experiments conducted on multi-regional and multi-crop horticultural remote sensing datasets demonstrated that the proposed method significantly outperformed comparative approaches, including traditional threshold-based methods, support vector machines, random forests, autoencoders, ConvLSTM, and temporal transformer models. In the dual task of horticultural crop growth anomaly detection and safety risk identification, an accuracy of approximately 0.91 and an F1 score of 0.88 were achieved, indicating higher anomaly recognition accuracy and more stable risk discrimination capability. Further anomaly-type awareness experiments showed that consistent performance was maintained across diverse real-world production scenarios, including climate stress, disease-induced anomalies, and management errors. Full article

Biosensors based on electrolyte-gated organic field-effect transistors (EGOFETs) have attracted considerable attention due to their advantages, including low cost, inherent signal amplification, and low-voltage operation. A critical step influencing sensing performance is the integration of specific receptors onto the device surface. Among various strategies, the covalent immobilization of biorecognition elements onto gold surfaces via thiol chemistry is one of the most widely used approaches. In this study, we report the optimization of a mixed self-assembled monolayer (SAM) composed of 11-mercaptoundecanoic acid (11-MUA) and 3-mercaptopropionic acid (3-MPA) for label-free detection of human IgG using EGOFETs. The quality of the SAM was systematically modulated by varying the total concentration from 10 to 400 mM and characterized using X-ray Photoelectron Spectroscopy (XPS), Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV), and Atomic Force Microscopy (AFM). The results revealed that a concentration of 50 mM yielded a densely packed and well-ordered monolayer. After covalent immobilization of anti-IgG antibodies via 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) chemistry and subsequent blocking with ethanolamine and bovine serum albumin (BSA), the functionalized gate electrodes were integrated into poly(3-hexylthiophene) (P3HT)-based EGOFETs. Electrical measurements demonstrated that EGOFET biosensors functionalized with the 50 mM SAM achieved optimal sensing performance. The devices exhibited a highly linear response (R² = 0.998) over a wide concentration range from 1 fM to 10 nM, with a LOD of 2.82 fM, and showed excellent selectivity against non-target immunoglobulins A and M (IgA and IgM). This SAM concentration optimization strategy provides a versatile approach for engineering high-performance EGOFET biosensors, with potential applicability to a broad range of disease biomarkers. Full article

Code smells are indicators of potential design problems in software systems and are commonly used to guide refactoring activities. Recent advances in representation learning have enabled the use of embedding-based models for analyzing source code, offering an alternative to traditional approaches based on manually engineered metrics. However, the effectiveness of different embedding representations for multiclass code smell detection remains insufficiently explored. This study presents an empirical comparison of embedding models for the automatic detection of three widely studied code smells: Long Method, God Class, and Feature Envy. Using the Crowdsmelling dataset as an empirical basis, source code fragments were extracted from the original projects and transformed into vector representations using two embedding approaches: a general-purpose embedding model and the code-specialized CodeBERT model. The resulting representations were evaluated using several machine learning classifiers under a stratified group-based validation protocol. The results show that CodeBERT consistently outperforms the general-purpose embeddings across multiple evaluation metrics, including balanced accuracy, macro F1-score, and Matthews correlation coefficient. Dimensionality reduction analyses using PCA and t-SNE further indicate that CodeBERT organizes code smell instances in a more structured latent representation space, which facilitates the separation of smell categories. In particular, CodeBERT achieved a macro F1-score of 0.8619, outperforming general-purpose embeddings (0.7622) and substantially surpassing a classical TF-IDF baseline (0.4555). These findings highlight the value of this study as a controlled multiclass evaluation of embedding representations and demonstrate the practical value of domain-specific representations for improving automated code smell detection and class separability in real-world software engineering scenarios. Full article

Rapid urbanization necessitates high-frequency monitoring of construction-ready bare land to timely detect and prevent illegal construction. However, the utility of optical imagery is often compromised in cloud-prone regions. While Synthetic Aperture Radar (SAR) offers all-weather capabilities, it struggles to distinguish construction-ready bare land from recently harvested agricultural land, leading to severe false alarms. To address the conflict between high-frequency monitoring and semantic identification, this study proposes the SAR-based Change Screening and Optical-Scene-Informed Identification (SCS-OI) framework. The first stage performs high-recall candidate screening based on SAR backscattering changes, while the second stage incorporates historical cloud-free optical imagery as semantic guidance, enabling refined identification without requiring synchronous optical data. Experiments in Guangzhou demonstrate that the framework achieves a False Alarm Rate of 13.31%, Recall of 90.63%, Precision of 74.81%, F1-score of 81.95%, and IoU of 69.43%. Compared with the SAR-only baseline (FR = 22.4%), the two-stage design reduces false alarms while maintaining high recall. Other deep learning baselines exhibit lower F1-scores (59–73%), highlighting the effectiveness of the overall framework. These results show that the proposed two-stage framework effectively integrates high-recall candidate screening and semantic-guided refinement, providing a robust solution for high-frequency monitoring of construction-ready bare land in cloud-prone regions of Guangzhou. Full article

To address ammonium nitrogen (NH₄⁺-N) and nitrite accumulation in intensive marine shrimp aquaculture, a marine recirculating aquaculture system (RAS) for Penaeus vannamei centered on a moving bed biofilm reactor (MBBR) was constructed to investigate the microbial basis of nitrogen removal. The results showed that the MBBR contributed most to NH₄⁺-N removal, demonstrating favorable nitrification potential under marine conditions (0.513 mg·L⁻¹·h⁻¹). The biofilm carrier formed a complete attached layer and developed a mature biofilm structure. Microbial community analysis revealed clear differentiation between the biofilm and sediment. The biofilm community was dominated by norank_f__Caldilineaceae (9.89%). Linear discriminant analysis effect size identified the nitrifying genus Nitrospira to be significantly enriched on the biofilm side (α = 0.05, linear discriminant analysis > 2.0). In addition, PICRUSt2-based functional prediction suggested a higher potential in biofilm than in sediment for ammonia oxidation and downstream nitrogen transformation, involving ammonia monooxygenase (EC:1.14.99.39), hydroxylamine dehydrogenase (EC:1.7.2.6), nitrate reductase (EC:1.7.99.4), and nitrite reductase (EC:1.7.2.1). Thus, this study provides a microbial basis and process strategy for P. vannamei RAS. Full article

Aluminium alloys are extensively considered in aviation and automobiles owing to their lightweight properties and favourable specific strength-to-weight ratio. Generally, the poor surface properties of these alloys limit their application, particularly in sliding conditions. To enhance the surface qualities, particularly the material’s wear resilient features, a unique surface modification process using electro-discharge coating (EDC) has been employed. This work investigates the optimisation of coating variables produced by the EDC technique utilising green compact electrodes composed of 50 wt.% tungsten disulfide (WS₂) and 50 wt.% copper (Cu) powder. The substrate material utilised was AA7075 alloy. The Taguchi–TOPSIS approach was employed to determine optimal EDC process variables, with pulse-on time (T_on), current (I_p), and pulse-off time (T_off). Wear rate (WR), surface roughness (SR), and friction coefficient (CoF) were used to assess the coating features. A wear study was performed with a pin-on-disc device with an undeviating sliding speed (0.25 m/s) and a 25 N load. The results revealed that the supreme features derived from the linear plots were I_p (4 A), T_on (80 µs), and T_off (5 µs). The ANOVA found that I_p had the utmost significant impact, accounting for 44.09%; T_off, 28.01%; T_on, 20.33%; and minimum error, 8.58%. A validation trial with perfect parameters returned values of 0.000179 mm³/Nm (WR), 0.204 (CoF), and 2.818 µm (SR). These findings are significantly better than those of the other coatings. The discrepancy among the estimated and experimental relative closeness in optimal settings is 6.34%, demonstrating that the Taguchi–TOPSIS method is more appropriate for multi-criteria optimisation. Full article

Objectives: To investigate the effects of Roxadustat and recombinant human erythropoietin (rHuEPO) on glycemic control and glycated hemoglobin (HbA1c) in non-dialysis type 2 diabetic kidney disease (DKD) patients with anemia. Methods: This retrospective study enrolled 449 patients, who were divided into three groups—the rHuEPO group (n = 252), the Roxadustat group (n = 102), and the switch group (n = 95)—in which patients were converted from rHuEPO to Roxadustat. All treatments lasted for more than three months. Changes in HbA1c and other indicators within groups as well as differences among groups were evaluated. Results: In the rHuEPO group, HbA1c levels decreased from 7.08 ± 1.19 to 6.41 ± 0.60 (p < 0.001), and they returned to baseline levels by 6–12 months (p > 0.05). In the Roxadustat group, HbA1c fluctuated but none of the differences reached statistical significance (p > 0.05). In the switch group, HbA1c decreased during rHuEPO treatment (p < 0.05) and returned to baseline after switching to Roxadustat (p > 0.05). No significant changes in blood glucose levels were observed in any group after treatment (p > 0.05). Multivariate linear regression analysis showed that changes in iron metabolism parameters, erythrocyte parameters, inflammatory markers, and glucose-lowering or lipid-lowering regimens had no significant effect on the change in HbA1c in the Roxadustat group (F = 0.834, p = 0.620), while the multivariate model of rHuEPO group also lacked statistical significance (F = 1.142, p = 0.170). After treatment, all three groups showed improvements in anemia, iron metabolism, renal function, inflammatory markers, and lipid profiles compared with baseline (p < 0.05). Additionally, further improvements in these parameters were observed after the transition from rHuEPO to Roxadustat (p < 0.05). Compared with rHuEPO group, the Roxadustat group exhibited significantly greater increases in hemoglobin, red blood cell count, total iron-binding capacity, transferrin, and serum iron (p < 0.05). Conclusions: In non-dialysis DKD patients with anemia, rHuEPO can significantly decrease HbA1c values, while Roxadustat does not. Roxadustat offers advantages over rHuEPO in terms of efficacy and assessment of glycemic control. Full article

Reliable textile recycling requires automated unfolding to expose hidden hard components such as zippers, buttons, and metal fasteners, which otherwise risk damaging machinery and compromising downstream processes. This paper presents the design and implementation of an automated textile unfolding system based on a dual-arm robotic manipulation framework. The system uses two Interbotix WidowX 250s 6-DoF robotic arms and an Intel RealSense L515 LiDAR camera for visual perception. The unfolding process consists of three stages: initial dual-arm stretching to reduce major folds, refinement through a second stretch targeting the lower region, and a machine-learning stage that employs a YOLOv11 framework trained on depth-encoded textile images, followed by a depth-gradient-based estimator for fold direction. The system applies an extremity-based grasping strategy that selects leftmost and rightmost textile points from a custom error-corrected depth map, enabling robust grasp point selection, and a fold direction estimation method based on depth gradients around the detected fold. The most confident fold region is selected, an unfolding direction is determined using depth ranking, and the textile is manipulated until a flat state is confirmed through depth uniformity. Experiments show that depth correction significantly reduces spatial error in the robot frame, while segmentation and extremity detection achieve high accuracy across varied fold configurations, and the YOLOv11n-based model reaches 98.8% classification accuracy, while fold direction is estimated correctly in 87% of test cases. By enabling robust, largely autonomous textile unfolding, the system demonstrates a practical approach that could support safer and more efficient automated textile recycling workflows. Full article

Citrus trees exhibit severe alternate bearing, resulting in significant annual yield fluctuations and posing substantial challenges to orchard management planning. Accurate citrus bud counting provides an effective solution by supplying essential data for tree-level and orchard-level yield prediction. However, citrus buds are extremely small (5–10 mm in diameter) and are frequently occluded by leaves during the flowering stage, which makes precise detection highly challenging in complex orchard environments. To address these challenges, this paper proposes a Contextual and Frequency-Aware Detector (CFADet) for robust citrus bud detection. Specifically, an Enhanced Feature Fusion (EFF) module is introduced in the neck to refine multi-scale feature aggregation and strengthen information flow for small targets. A Contextual Boundary Enhancement Module (CBEM) is designed to capture surrounding contextual cues and enhance boundary representation through dimensional interaction and max-pooling operations. To suppress background interference, a Frequency-Aware Module (FAM) is developed to adaptively recalibrate frequency components in the amplitude spectrum, thereby enhancing target features while reducing background noise. In addition, Spatial-to-Depth Convolution (SPDConv) is employed to reconstruct the backbone to preserve fine-grained bud features while reducing model parameters. Experimental results show that CFADet achieves 81.1% precision, 80.9% recall, 81.0% F1-score, and 87.8% mAP, with stable real-time performance on mobile devices in practical orchard scenarios. This study presents a preliminary investigation into robust citrus bud detection in real-world orchard environments and provides a promising technical foundation for intelligent orchard monitoring and early yield estimation, while further validation on larger and more diverse datasets is still required. Full article

Breast cancer (BC) requires the evaluation of tumor aggressiveness features to guide treatment decisions. Biopsy-derived prognostic information may differ from surgical histopathology due to tumor heterogeneity. Hybrid PET/MRI can provide additional information for tumor characterization, supporting initial therapy planning and prognosis. In this work, we acquired 157 BC patients using a hybrid PET/MRI scanner. The PET data were combined with ADC and semi-quantitative DCE-MRI metrics to derive “hybrid PET/MRI parameters.” Pathological data such as tumor grade, hormone receptors, proliferation index (Ki67), and surrogate molecular subtype were collected, and we evaluated their associations with hybrid imaging, also comparing with the PET and MRI data analyzed separately. Ki67 showed moderate correlations with PET, ADCmin, and most hybrid parameters. The PET and hybrid data differentiate histopathological factors, while ADCmin differentiates G1 vs. G2 and luminal A vs. luminal B. In the ROC analysis, hybrid SUVmax/ADCmin shows better performance to predict luminal B from luminal A (AUC 0.720, sensitivity 73.1%, specificity 63.2%, PPV 54.3%, NPV 79.7%) than SUVmean alone. Our findings suggest that these novel hybrid PET/MRI parameters may help the characterization of tumor tissue in IDC. However, a multivariate analysis is needed to confirm our preliminary results. Full article