Search Results (28,892)

This study aimed to disentangle the relative influence of inherent soil properties and annual starter P fertilization on active carbon (C) pools and C-, nitrogen (N)-, and phosphorus (P)-cycling enzyme activities in silage corn production systems with high-legacy P. Six fields with Mehlich-3 P ranging from 53.5 to 332 mg kg⁻¹ were investigated in 2020 and 2021 in the Fraser Valley, Canada. The experiments at each site consisted of five starter P rates (0, 5, 10, 15, and 20 kg P ha⁻¹ as triple super phosphate) arranged in a randomized complete block design with four replicates. Soil samples were collected at the V3 and V6 stages of silage corn and analyzed for active C, soil enzyme activities, and chemical properties. N-acetyl-β-glucosaminidase varied significantly across the six sites, suggesting substantial differences in the rate of C and N cycling. For instance, in 2020, N-acetyl-β-glucosaminidase was similar at Sites 1 and 2 at V6 and was approximately three times (514.51 pmol MUF g⁻¹ soil h⁻¹) higher than at Site 3 (170.29 pmol MUF g⁻¹ soil h⁻¹). Similarly, in 2021, a 2.8-fold higher MBC observed at Site 4 at V6, compared with the averages of Sites 5 and 6, further confirms an active C pool. Meanwhile, sites with the lowest MBC concentrations were linked to acidic soils (pH 5.3), and a negative correlation between inherent site–year characteristics and enzyme activities confirm enzymes repression. Acid phosphatase at Site 4 was 3-fold higher than at Site 5 and Site 6, while alkaline phosphatase was detected only at Site 4. We conclude that long-term soil conditions are the main factors influencing biological functionality, thereby overshadowing transient fertilization. This indicates that Fraser Valley farmers can prioritize long-term soil health management and safely reduce starter P applications in these high-legacy systems. Full article

Background/Objectives: Front-of-pack labelling systems such as Nutri-Score are promoted as public health tools to guide consumers towards healthier food choices. However, their capacity to accurately signal sugar content in child-targeted foods—a key determinant of dental caries risk—remains poorly characterised. This study aimed to evaluate whether Nutri-Score category reliably reflects sugar content in pre-packaged foods marketed to children, and to discuss the implications for paediatric oral health. Methods: A cross-sectional observational study analysed the nutritional labels of 100 pre-packaged foods directed at the paediatric population, all displaying a Nutri-Score label, selected from three major supermarket chains in Valencia, Spain. Products were grouped into eight predefined food categories. Sugar content (g/100 g or 100 mL), Nutri-Score category (A–E), and ordinal position of sugar in the ingredients list were recorded. Global association between Nutri-Score grade and sugar content was evaluated using Spearman’s rank correlation coefficient; category-level analyses used the Kruskal–Wallis or Mann–Whitney U test as appropriate. Results: A moderate statistically significant positive correlation was found between Nutri-Score grade and sugar content across all 100 products (ρ  =  0.515, p  <  0.001). In the exploratory category-level analyses, suggestive differences were observed in biscuits (H  =  8.72, p  =  0.033), milks and milk drinks (H  =  9.02, p  =  0.029), and desserts (H  =  8.31, p  =  0.016); none of these p-values survived Bonferroni correction for multiple comparisons (adjusted α  =  0.006). Notably, some products rated A and B contained high sugar levels: one A-rated breakfast cereal reached 22.4 g/100 g, and the highest B-rated product (a flavoured milk drink) contained 22.1 g/100 g. No significant association was detected in cereals, breads, dairy products, juices, or frozen foods. Conclusions: Nutri-Score demonstrated limited discriminatory ability to identify high-sugar child-targeted foods consistently across food categories. These findings support recommending that paediatric dental practitioners advise caregivers to evaluate sugar content and ingredients lists beyond front-of-pack grading. Further regulatory refinement of the algorithm to specifically weight added sugar exposure in child-targeted products may be warranted. Full article

Background and Objectives: Hyperphosphatemia is a key component of chronic kidney disease-mineral and bone disorder and is associated with higher rates of cardiovascular events and mortality in patients undergoing dialysis. Phosphate binders are essential for phosphorus reduction, and their evaluation relies on several factors, such as chemical composition, binding capacity, and safety profile. Unfortunately, long-term management is hindered by high pill burden and poor adherence. Magnesium phosphate binders have proven effectiveness in reducing phosphate levels. Nevertheless, despite their efficacy, they are not widely used in clinical settings because of concerns related to their use. This study assessed the efficacy of low-dose magnesium carbonate as a phosphate binder in patients undergoing chronic dialysis, focusing on its biochemical control, tolerability, and cost-effectiveness. Materials and Methods: A prospective observational study was conducted on 54 hemodialysis patients with end-stage renal disease at a single dialysis center. Patients were taking either 250 mg magnesium carbonate or 2400 mg sevelamer carbonate for 3 months. Results: Both groups showed decreased phosphorus levels, with a 14.2% significant reduction in the magnesium carbonate group (p < 0.001) and a 5.1% reduction in the sevelamer carbonate group. At the end of the study, no significant differences were observed between the groups (p = 0.682). In the magnesium carbonate group at month 3, compared to baseline, no significant differences were detected in other laboratory parameters reflecting calcium–phosphorus metabolism (Ca—p = 0.681, PTH—p = 0.126). Simultaneously, good compliance without clinically relevant gastrointestinal side effects was observed, including the absence of clinically significant hypermagnesemia. Conclusions: The phosphorus-lowering potential of low-dose magnesium carbonate is non-inferior to that of low-dose sevelamer carbonate. However, its favorable safety profile, low incidence of adverse effects, and cost-effectiveness make it a promising option in clinical practice, further highlighting the need for better recognition by physicians. Full article

Driven by the dual-carbon target strategy, the new energy vehicle industry has achieved large-scale and rapid development. As the core protective component of power batteries, the surface quality of battery shells directly determines the operational safety and reliability of batteries. However, defects such as scratches, pits, and cracks easily occur on battery shells during forming processes, including stamping and deep drawing. Traditional manual detection suffers from bottlenecks, such as high labor intensity, low detection efficiency, and high false detection rates, making it difficult to adapt to the large-scale and high-cycle production requirements of modern industry. Firstly, this study systematically elaborates the material system, preparation process, and defect formation mechanism of battery shells, and clarifies the coupling mechanisms of material properties, process parameters, and equipment and environmental conditions for defect evolution. Subsequently, it compares and analyzes the principles, advantages and disadvantages, and applicable scenarios of traditional machine-vision- and deep-learning-based detection technologies, and focuses on analyzing the application performance and optimization paths of single-stage and two-stage object detection algorithms in shell defect recognition. Furthermore, it addresses the core challenges of deep-learning-based battery shell defect detection technologies in data, algorithm deployment, detection dimensions, and other aspects, and proposes targeted optimization strategies. Finally, the development directions, such as system integration and online learning, are forecasted. This study can provide theoretical support and technical references for the intelligent manufacturing of battery shell stamping and forming, as well as for surface defect detection. Full article

In a closed-inventory large language model (LLM) system such as Online-CADCOM, which recommends engineering tools from a verified inventory, we measure inventory non-compliance, that is, a mention-level event in which the model recommends a tool not present in the verified inventory. We use this inventory-relative sense of hallucination throughout: an out-of-inventory mention may be a fabricated tool or a real commercial tool absent from the curated inventory, so the metric reports inventory non-compliance rather than factual fabrication. We evaluate a three-mechanism mitigation stack consisting of database-grounded context injection, fixed vocabulary constraints, and enforced JavaScript Object Notation (JSON) output across three commercial LLM providers (OpenAI, Anthropic, Google), two model generations, and two output modes (standard and reasoning), totaling 6912 Application Programming Interface (API) calls over 12 configurations. Under a recall-equalized detector adopted as the primary metric, the inventory non-compliance rate, which we denote the hallucination rate (HR) following common usage, decreases from roughly 69–80% to 4–13% under the full architecture. The cross-provider average is similar across the two generations tested (8.5% Generation 1 (Gen1), 6.9% Generation 2 (Gen2)), although per-provider directions diverge. We also examine the C3 configuration, in which only JSON output enforcement is active without grounding. A naive detector reports a large hallucination increase over the unconstrained baseline (+10.1 percentage points (pp) Gen1, +15.1 pp Gen2), but we show this gap is largely a detection-format artifact: structured JSON fields make out-of-inventory tools easy to extract, whereas the same real tools are frequently missed in free text. Under a recall-equalized detector the gap narrows to +2.6 pp (Gen1) and +4.8 pp (Gen2) and remains statistically significant only for two current-generation models, indicating a small, current-generation effect rather than a universal one. Reasoning-mode models provide no statistically significant improvement under architectural constraints. A frequency-weighted audit shows that the majority of remaining out-of-inventory mentions correspond to real engineering tools absent from the platform’s inventory. Under the full architecture, roughly half of responses (pooled $P_{\mathrm{any}}\approx 49.5\%$) still contain at least one such mention, indicating that handling unseen tools remains an open challenge for closed-inventory recommendation systems. Our evidence comes from a single engineering platform with four related electronic-design and power-electronics domains, so the findings characterize this setting rather than recommendation domains in general. Full article

Anxiety disorders affect more than 301 million people worldwide and remain the most prevalent class of mental health conditions. Detection methods have traditionally relied on subjective self-reports, which fail to capture the dynamic physiological signatures of anxiety in daily life. In this longitudinal study, continuous wearable monitoring was conducted with 67 working professionals over nine months, yielding 44,443 h of multimodal data segmented into 890,494 five-minute windows. An unsupervised approach was applied, and ten distinct autonomic states were identified through spectral clustering without the use of emotion labels during clustering. Among these, a rare dysregulated state was discovered, characterized by extreme peripheral cooling ($d=-6.77$), tachycardia ($d=+4.41$), and heightened electrodermal activity, showing a 31-fold enrichment for self-reported anxiety ($p=0.0022$). Beyond this extreme signature, anxiety was expressed heterogeneously across four physiological phenotypes ranging from acute dysregulation to mild perturbation. External validation with the WESAD dataset confirmed generalizability, while the absence of the rare state in laboratory stress paradigms highlighted its specificity to ecological anxiety. By integrating unsupervised discovery with post hoc self-report validation, this work advances our theoretical understanding of autonomic affect and provides a methodological foundation for scalable workplace mental health monitoring. Full article

This study was conducted to determine the optimal mineralization enhancement period for recycled aggregates, and to elucidate the mechanisms underlying the mineralization enhancement of recycled aggregates and the self-healing of concrete cracks. Microbial-induced calcium carbonate precipitation enables the self-healing of concrete cracks: microbial carriers can effectively increase the survival rate of microorganisms within the concrete matrix, thereby enhancing the self-healing performance of the concrete. However, current carriers suffer from poor mechanical properties, poor compatibility with cement-based materials, and high costs. This study proposed a crack-self-healing concrete based on a mixed culture of microorganisms immobilized in recycled aggregate, and investigated the effects of the time of recycled aggregate incorporation on the concrete’s compressive strength and self-healing performance. The results showed that the optimal adsorption and incubation times for the recycled aggregates were 15 min and 9 days, respectively. Following mineralization and reinforcement, the water absorption and crushing index of the recycled aggregates was 11.4% and 20.4%, respectively. Moreover, the precipitates at the concrete cracks were in the form of regular cubes and clusters, and the crystals were calcite and aragonite. Small amounts of phosphorus were detected, originating from extracellular polymers produced by microbial metabolism, indicating that the organic matrix was involved in the crystal nucleation and growth processes. The compressive strength of the concrete increased by 35%. After repair and curing, the crack healing rate of the concrete reinforced with microorganisms immobilized on the recycled aggregates reached 70%. Full article

UAV-based remote sensing excels in rapid response, high timeliness, simple operation, and high degrees of automation, and has been widely applied for geological hazard monitoring. Deep learning methods based on unitemporal UAV images can only analyze the static appearance of a scene, while bitemporal change detection can capture the dynamic evolution of hazards; however, due to diverse geological landforms and topography, environmental noises such as vegetation cover, and dynamic weather conditions, change detection of geological hazards from UAV images based on traditional deep learning technology is not always effective. Therefore, there is an urgent need to utilize large vision-language models (LVLMs) to further improve the accuracy and robustness of the change detection model. Motivated by this, this paper proposes a novel remote sensing model for geological hazard monitoring, referred to as CLIFF (CLIP-BIT-EfficientNet), based on the multi-modal LVLM Contrastive Language–Image Pre-training (CLIP), the change detection network Bitemporal Image Transformer (BIT), and the classification network EfficientNet, along with corresponding datasets and model fine-tuning strategies. The proposed transfer fusion module bridges the CLIFF and BIT networks by aligning their feature distributions and dimensions, allowing the general knowledge of the LVLM and the task-specific knowledge of the learnable branch to reinforce each other. Furthermore, this integrated pipeline addresses the scarcity of labeled hazard data by allowing the BIT to train on larger public datasets, while fine-tuning EfficientNet on smaller hazard-classification datasets within the change area, making the approach more efficient and reliable than direct classification methods. Experimental results show that the proposed CLIFF algorithm outperforms state-of-the-art deep learning algorithms such as LightCDNet and ChangeFormer, with an IoU of 75.74% and an F1 score of 0.8689 for change detection. Meanwhile, CLIFF has an overall accuracy rate of 86.89% in identifying geological hazards along gas pipelines, such as crude oil spills, collapses, landslides, and floods, with per-class accuracies of 87.32% and 86.17% for crude oil spills and landslides, respectively. Full article

Memory-bank methods such as PatchCore are widely used in industrial quality control for visual anomaly detection since they require no training, are fast to deploy and achieve strong accuracy. However, they are memory-intensive. Furthermore, a single production line typically involves different products or cameras, so using a single anomaly detection method with a shared nearest-neighbor memory bank is attractive since it simplifies deployment and makes new products easy to add. Nevertheless, embeddings from different products/cameras can interfere during retrieval, causing what we call “memory pollution”. In this work, we study this effect through a new diagnostic framework, which involves: (1) a new metric, the wrong-neighbor rate (WNR), which measures how often a query’s nearest neighbor belongs to a different product; (2) an empirically validated phenomenon, “oracle inversion”, where querying only the product’s own data can underperform the shared bank under a fixed memory budget; (3) a first-order analytical model of the WNR, which predicts how pollution grows with product count and memory budget; and (4) a minimal training-free router that removes the effect of memory pollution. Our results show that our system performs robustly across different datasets and backbones, with up to 25× memory reduction, which makes our framework attractive for industrial applications. Full article

Real-time recognition of the transient interface opening between a nickel starting sheet and its titanium starter plate is required to stop high-frequency tamping at the correct instant. This study proposes Yolov11_tapcheck, a deployment-oriented detector based on YOLO11n, and implements a field real-time detection system for nickel starting sheet pre-stripping. To address small target scale, weak texture, reflective background interference, and motion blur, the model introduces efficient multi-scale attention (EMA) and reconstructs the neck as a BiFPN-inspired bidirectional feature-fusion path. A tapcheck dataset was established from production-line images, and the method was evaluated with image-level accuracy, latency, and field control metrics. Compared with the original YOLO11n, Yolov11_tapcheck improved mAP50 from 95.5% to 98.1% and F1 from 93.7% to 97.5%. The average model-pipeline latency was 7.1 ms; when MQTT delivery, OPCSERVER writing, PLC feedback timestamp comparison, and 1 ms polling were included, the average frame-to-PLC acknowledgement latency was 10.24 ms, with a maximum of 11.58 ms, remaining below the 13.333 ms frame interval in 1000 logged samples. Two field stages further showed that the opening success rate for cycles 3–8 increased from about 60–65% to more than 90%, while opening detection success increased from 90% to 100%. These results indicate that the proposed method can support real-time opening recognition and closed-loop control in nickel starting sheet stripping. Full article

Deep learning-driven medical image analysis has become a critical auxiliary tool for clinical oral disease diagnosis. However, most existing caries detection methods based on YOLO series models are validated on single-lesion datasets, leading to generally high missed-detection rates and unsatisfactory localization accuracy in complex dental radiographic scenarios where multiple dental conditions and restorations coexist. To address this limitation, this paper proposes YOLO-ASPE, an improved YOLOv11n lightweight framework for tiny-carious-lesion detection. It integrates a P2 high-resolution detection branch, a multi-scale sequential feature fusion module, a lightweight SE channel attention mechanism, and EIoU bounding box regression loss. Experiments were conducted on a public eight-category dental radiographic dataset with caries as the sole detection target. The results show that YOLO-ASPE achieved a Caries AP@0.5 of 68.7%, 10.5 percentage points higher than the baseline model, with a real-time inference speed of 312 FPS. This work provides a lightweight technical reference for the computer-assisted screening of dental caries, although further external validation is still required before clinical application. Full article

Boar sperm freezability (SF) is an economically important trait that influences reproductive efficiency and genetic improvement in pigs. However, its genetic basis remains poorly understood. In this study, semen samples from 382 Duroc and 151 Yorkshire boars were evaluated for sperm motility and recovery rate, and all individuals were genotyped using an 80K SNP array. Within-breed GWAS were first conducted, followed by a meta-analysis integrating the GWAS results from both boars. Individuals were classified into GSF and PSF groups based on sperm recovery rate for subsequent selection signature analysis. The results showed that Duroc boars exhibited significantly higher SF than Yorkshire boars. Heritability estimates for SF were moderate, with values of 0.35 in Duroc and 0.30 in Yorkshire. GWAS identified 10 significant SNPs in Duroc and 33 in Yorkshire associated with SF. Meta-analysis further detected 12 significant SNPs, annotated to candidate genes such as CCDC181, PARN, SOX9, and NCKAP5L. Association analysis identified ten representative variants significantly correlated with sperm recovery rate, with variants in PARN and MAP2K6 showing strong additive effects. Selection signature analysis revealed multiple genomic regions under differential selection between GSF and PSF groups, identifying several candidate genes associated with SF. Functional enrichment analysis indicated that these genes are mainly involved in spermatogenesis, flagellar motility, cellular stress response, and cold adaptation pathways. Overall, this study provides novel insights into the genetic architecture of boar SF and identifies potential molecular markers for genetic improvement and functional genomic studies in pigs. Full article

Sixth-generation (6G) networks require advanced non-orthogonal multiple access (NOMA) schemes to support the technical requirements of dense massive machine-type communications (mMTC). While sparse-code multiple access (SCMA) improves uplink spectral efficiency, its operation within the complex two-dimensional domain ($\mathbb{C}$) generates spatial congestion and high error rates under high-load scenarios. Furthermore, when using dual-polarization transceivers, cross-polarization discrimination leakage is not efficiently exploited because it operates in a conventional 2D environment. This work proposes a hypercomplex transmission architecture, called quaternionic SVD-SCMA (Q-SVD-SCMA), which maps SCMA codewords to a quaternionic group ($Q_8$) in ${\mathbb{R}}^4$. The proposed scheme uses purely imaginary spatial rotators to project overlapping signals onto mutually orthogonal geometric subspaces, thus mitigating interference between users. On the receiver side, a quaternionic sphere decoder (Q-SD) is proposed to evaluate the minimum quaternionic Euclidean distance (MQED) to provide near-optimal detection. Computational simulations are performed under a doubly polarized Rayleigh fading channel with energy normalization to decouple arbitrary power-scale topological gains. To evaluate system performance, both the symbol error rate (SER) and the bit error rate (BER) are used. The results obtained demonstrate that Q-SVD-SCMA effectively transforms Cross-Polarization Discrimination (XPD) leakage into spatial diversity gain. The hypercomplex 4D architecture proposed in this work eliminates the interference-induced error threshold and limits the bit error penalty through multidimensional Gray mapping, providing a scalable and highly reliable physical layer framework for overloaded 6G scenarios, unlike its conventional 2D predecessors (C-SCMA and SVD-SCMA). Full article

Accurate streamflow prediction is an important part of disaster management, as forecasting flow rates is an indispensable element of early warning systems, yet it remains challenging due to the complexity and nonlinearity of climatic inputs. This study evaluated the performances of Long Short-Term Memory (LSTM) and random forest (RF) machine learning algorithms for daily streamflow prediction across sixteen hydroclimatically diverse basins in the contiguous United States using the CAMELS dataset. The models were trained on five climatic features augmented with antecedent streamflow lag features at 7-, 14-, and 30-day intervals. The random forest algorithm demonstrated a better accuracy, achieving an average Nash–Sutcliffe efficiency (NSE) of 0.755 compared to 0.632 for the LSTM model. Both models performed well in the snowmelt-dominated basins and were least effective in flashy humid regimes. Additionally, both models exhibited high precision for flood detection, with accuracy rates exceeding 88% for distinguishing flood events and F1 scores of 0.734 and 0.797 for LSTM and RF, respectively. These results recommend RF for operational streamflow forecasting across hydroclimatically diverse settings and LSTM for perennial snowmelt- and groundwater-influenced catchments where long-range temporal dependencies govern runoff generation. Full article

Background: Accurate preoperative localization of hyperfunctioning parathyroid glands remains challenging, particularly in secondary hyperparathyroidism where multiglandular disease may compromise scintigraphic performance. Methods: Our study evaluated biochemical, morphometric, and histopathological predictors of ^99mTc-sestamibi scintigraphy detectability in both primary and secondary hyperparathyroidism. The study group included a total of 162 patients with primary and secondary hyperparathyroidism who underwent dual-phase ^99mTc-sestamibi scintigraphy followed by parathyroidectomy. Demographics, biochemical parameters, histopathological features, lesion volume and scintigraphic findings were assessed at patient and lesion level. Results: In primary hyperparathyroidism, adenomas were larger and more frequently detected than hyperplastic glands. Lesion volume and solid growth pattern were found as positive predictors of sestamibi uptake. In secondary hyperparathyroidism, nodular hyperplasia was associated with larger volume, higher cellularity, and more frequent localizing studies. Upper quadrant position and diffusely hyperplastic lesions were associated with higher lesion miss rates, while lesion volume increased the likelihood of detection. Conclusions: Our findings highlight that ^99mTc-sestamibi scintigraphy performance is strongly influenced by lesion volume, histopathological architecture and anatomical position, underscoring the needs for cautious interpretation of negative or incomplete scans, especially in secondary hyperparathyroidism. Full article