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17 pages, 29159 KiB  
Article
REW-YOLO: A Lightweight Box Detection Method for Logistics
by Guirong Wang, Shuanglong Li, Xiaojing Zhu, Yuhuai Wang, Jianfang Huang, Yitao Zhong and Zhipeng Wu
Modelling 2025, 6(3), 76; https://doi.org/10.3390/modelling6030076 (registering DOI) - 4 Aug 2025
Abstract
Inventory counting of logistics boxes in complex scenarios has always been a core task in intelligent logistics systems. To solve the problems of a high leakage rate and low computational efficiency caused by stacking, occlusion, and rotation in box detection against complex backgrounds [...] Read more.
Inventory counting of logistics boxes in complex scenarios has always been a core task in intelligent logistics systems. To solve the problems of a high leakage rate and low computational efficiency caused by stacking, occlusion, and rotation in box detection against complex backgrounds in logistics environments, this paper proposes a lightweight, rotated object detection model: REW-YOLO (RepViT-Block YOLO with Efficient Local Attention and Wise-IoU). By integrating structural reparameterization techniques, the C2f-RVB module was designed to reduce computational redundancy in traditional convolutions. Additionally, the ELA-HSFPN multi-scale feature fusion network was constructed to enhance edge feature extraction for occluded boxes and improve detection accuracy in densely packed scenarios. A rotation angle regression branch and a dynamic Wise-IoU loss function were introduced to further refine localization and balance sample quality. Experimental results on the self-constructed BOX-data dataset demonstrate that the REW-YOLO achieves 90.2% mAP50 and 130.8 FPS, with a parameter count of only 2.18 M, surpassing YOLOv8n by 2.9% in accuracy while reducing computational cost by 28%. These improvements provide an efficient solution for automated box detection in logistics applications. Full article
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19 pages, 2359 KiB  
Article
Research on Concrete Crack Damage Assessment Method Based on Pseudo-Label Semi-Supervised Learning
by Ming Xie, Zhangdong Wang and Li’e Yin
Buildings 2025, 15(15), 2726; https://doi.org/10.3390/buildings15152726 - 1 Aug 2025
Viewed by 192
Abstract
To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to [...] Read more.
To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article
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14 pages, 892 KiB  
Article
Medication Adherence in Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation
by Hermioni L. Amonoo, Emma D. Wolfe, Emma P. Keane, Isabella S. Larizza, Annabella C. Boardman, Brian C. Healy, Lara N. Traeger, Corey Cutler, Stephanie J. Lee, Joseph A. Greer and Areej El-Jawahri
Cancers 2025, 17(15), 2546; https://doi.org/10.3390/cancers17152546 - 1 Aug 2025
Viewed by 129
Abstract
Introduction: Medication adherence is essential for treatment and recovery following hematopoietic stem cell transplantation (HSCT). However, limited data exist on the most effective methods to measure adherence and the factors influencing it in HSCT patients. Materials and Methods: A prospective longitudinal [...] Read more.
Introduction: Medication adherence is essential for treatment and recovery following hematopoietic stem cell transplantation (HSCT). However, limited data exist on the most effective methods to measure adherence and the factors influencing it in HSCT patients. Materials and Methods: A prospective longitudinal study assessed immunosuppressant medication adherence in 150 patients with hematologic malignancies undergoing allogeneic HSCT. Adherence was assessed using pill counts, immunosuppressant medication levels, patient-reported medication logs, and the Medication Adherence Response Scale-5 (MARS-5) at 30, 100, and 180 days post-HSCT. We evaluated adherence rates, agreement between methods, and sociodemographic and clinical predictors. From patient-reported logs, we calculated dose adherence (comparing reported doses to expected doses) and timing adherence (comparing medication intake within ±3 h of the prescribed time). Kappa analysis assessed agreement among methods. Results: Of 190 eligible patients, 150 (78.9%) enrolled. The mean age was 57.5 years (SD = 13.5); 41.3% (n = 62) were female, 85.3% (n = 128) were non-Hispanic White, and 73.3% (n = 110) were married or living with a partner. Medication adherence varied across the three timepoints and by measurement type: 52–64% (pill counts), 18–24% (medication levels), 96–98% (medication log dose adherence), 83–84% (medication log timing adherence), and 97–98% (MARS−5). There was minimal agreement between measures (Kappa range: 0.008–0.12). Conclusions: Despite the feasibility of leveraging objective and patient-reported measures to assess medication adherence in HSCT patients, there was little agreement between these measures. Patient-reported measures showed high adherence, while objective measures like pill counts and medication levels revealed more modest adherence. The complexity of medication regimens likely contributes to this discrepancy. A rigorous approach to understanding medication adherence in the HSCT population may entail both objective and subjective measures of medication adherence. Full article
(This article belongs to the Section Clinical Research of Cancer)
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23 pages, 7166 KiB  
Article
Deriving Early Citrus Fruit Yield Estimation by Combining Multiple Growing Period Data and Improved YOLOv8 Modeling
by Menglin Zhai, Juanli Jing, Shiqing Dou, Jiancheng Du, Rongbin Wang, Jichi Yan, Yaqin Song and Zhengmin Mei
Sensors 2025, 25(15), 4718; https://doi.org/10.3390/s25154718 - 31 Jul 2025
Viewed by 237
Abstract
Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield [...] Read more.
Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R2 values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments. Full article
(This article belongs to the Section Smart Agriculture)
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26 pages, 62045 KiB  
Article
CML-RTDETR: A Lightweight Wheat Head Detection and Counting Algorithm Based on the Improved RT-DETR
by Yue Fang, Chenbo Yang, Chengyong Zhu, Hao Jiang, Jingmin Tu and Jie Li
Electronics 2025, 14(15), 3051; https://doi.org/10.3390/electronics14153051 - 30 Jul 2025
Viewed by 157
Abstract
Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with [...] Read more.
Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the AP50 of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article
(This article belongs to the Section Artificial Intelligence)
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11 pages, 526 KiB  
Article
Prognostic Factors for 28-Day Mortality in Pediatric Patients with Acute Leukemia and Candidemia Following Intensive Chemotherapy: A Retrospective Study
by Tran Thi Kieu My, Hoang Thi Hong, Mai Lan, Tran Quynh Mai, Dang Hoang Hai and Ta Thi Dieu Ngan
Hematol. Rep. 2025, 17(4), 38; https://doi.org/10.3390/hematolrep17040038 - 30 Jul 2025
Viewed by 196
Abstract
Background/Objective: Candidemia is a serious complication following intensive chemotherapy and is associated with high mortality in pediatric patients. This study aimed to identify the factors associated with 28-day mortality in pediatric patients with candidemia. Methods: We retrospectively analyzed 63 pediatric patients diagnosed with [...] Read more.
Background/Objective: Candidemia is a serious complication following intensive chemotherapy and is associated with high mortality in pediatric patients. This study aimed to identify the factors associated with 28-day mortality in pediatric patients with candidemia. Methods: We retrospectively analyzed 63 pediatric patients diagnosed with acute leukemia and candidemia following intensive chemotherapy. Clinical characteristics, laboratory findings, and epidemiological data were collected. Antifungal susceptibility data were available for 60 patients. Kaplan–Meier survival analysis was used to estimate the 28-day mortality rate, and Cox regression was performed to identify prognostic factors. Results: The 28-day mortality rate among the 63 patients (57.1% male, median age 9.74 years) was 36.5%. Candida tropicalis was the predominant species (96.8%). Antifungal susceptibility rates were 100% for amphotericin B and caspofungin and 22.2% for fluconazole. The factors independently associated with reduced 28-day mortality were an absolute lymphocyte count (ALC) ≥ 0.2 G/L at the time of candidemia diagnosis (5.3% vs. 50% mortality; hazard ratio [HR] = 0.08; 95% confidence interval [CI], 0.01–0.61), the use of antifungal prophylaxis (AFP) (26.3% vs. 52%; HR 0.31; 95% CI, 0.13–0.74), and granulocyte transfusion (GTX) combined with granulocyte colony-stimulating factor (G-CSF) (20% vs. 47.4%; HR = 0.31; 95% CI, 0.11–0.85). Conclusions: Our findings suggest that an ALC ≥ 0.2 G/L, AFP, and the administration of a GTX combined with G-CSF may be considered favorable prognostic factors. Full article
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14 pages, 661 KiB  
Article
Longevity and Culling Reasons in Dairy Herds in Southern Brazil
by Rodrigo de Almeida, Sidneia de Paula, Marianna Marinho Marquetti, Milaine Poczynek, Delma Fabíola Ferreira da Silva, Rodrigo Barros Navarro, Altair Antonio Valloto, José Augusto Horst and Victor Breno Pedrosa
Animals 2025, 15(15), 2232; https://doi.org/10.3390/ani15152232 - 29 Jul 2025
Viewed by 175
Abstract
This study aimed to evaluate cow longevity and identify the main culling reasons in dairy herds in Southern Brazil. Two data sets from 26 predominantly confined Holstein herds were analyzed over a 10-year period (2007–2016). The first included 11,150 cows that were culled, [...] Read more.
This study aimed to evaluate cow longevity and identify the main culling reasons in dairy herds in Southern Brazil. Two data sets from 26 predominantly confined Holstein herds were analyzed over a 10-year period (2007–2016). The first included 11,150 cows that were culled, died, or sold, and the second comprised 636,739 cows for demographic analysis. The average annual culling rate was 24.2%, mainly due to reproductive disorders (34.0%), mastitis/high somatic cell count (20.4%), and feet and leg problems (17.9%). Involuntary causes represented 89.5% of all culling. The death rate averaged 3.8%, with the most frequent causes being unknown (27.3%), other reasons (25.6%), tick fever (10.2%), and accidents/injuries (10.0%). Larger herds had higher culling rates than smaller ones (26.2% vs. 22.8%; p = 0.04), as did higher-producing herds compared to lower-producing ones (25.7% vs. 22.0%; p = 0.02). Cows with ≥5 calvings were culled more often (p < 0.01) than those in earlier lactations. Culling was lowest (p < 0.02) in spring and highest (p < 0.01) during early (0–60 d) and late (>420 d) lactation. Herds with a higher proportion of older cows had slightly lower milk yields (p < 0.01), indicating longevity does not always enhance productivity. Full article
(This article belongs to the Section Cattle)
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13 pages, 3812 KiB  
Article
Generation of Four-Beam Output in a Bonded Nd:YAG/Cr4+:YAG Laser via Fiber Splitter Pumping
by Qixiu Zhong, Dongdong Meng, Zhanduo Qiao, Wenqi Ge, Tieliang Zhang, Zihang Zhou, Hong Xiao and Zhongwei Fan
Photonics 2025, 12(8), 760; https://doi.org/10.3390/photonics12080760 - 29 Jul 2025
Viewed by 159
Abstract
To address the poor thermal performance and low output efficiency of conventional solid-state microchip lasers, this study proposes and implements a bonded Nd:YAG/Cr4+:YAG laser based on fiber splitter pumping. Experimental results demonstrate that at a 4.02 mJ pump pulse energy and [...] Read more.
To address the poor thermal performance and low output efficiency of conventional solid-state microchip lasers, this study proposes and implements a bonded Nd:YAG/Cr4+:YAG laser based on fiber splitter pumping. Experimental results demonstrate that at a 4.02 mJ pump pulse energy and a 100 Hz repetition rate, the system achieves four linearly polarized output beams with an average pulse energy of 0.964 mJ, a repetition rate of 100 Hz, and an optical-to-optical conversion efficiency of 23.98%. The energy distribution ratios for the upper-left, lower-left, upper-right, and lower-right beams are 22.61%, 24.46%, 25.50%, and 27.43%, with pulse widths of 2.184 ns, 2.193 ns, 2.205 ns, and 2.211 ns, respectively. As the optical axis distance increases, the far-field spot pattern transitions from a single circular profile to four fully separated spots, where the lower-right beam exhibits beam quality factors of Mx2 = 1.181 and My2 = 1.289. Simulations at a 293.15 K coolant temperature and a 4.02 mJ pump energy reveal that split pumping reduces the volume-averaged temperature rise in Nd:YAG by 28.81% compared to single-beam pumping (2.57 K vs. 3.61 K), decreases the peak temperature rise by 66.15% (6.97 K vs. 20.59 K), and suppresses peak-to-peak temperature variation by 78.6% (1.34 K vs. 6.26 K). Compared with existing multi-beam generation methods, the fiber splitter approach offers integrated advantages—including compact size, low cost, high energy utilization, superior beam quality, and elevated damage thresholds—and thus shows promising potential for automotive multi-point ignition, multi-beam single-photon counting LiDAR, and laser-induced breakdown spectroscopy (LIBS) online analysis. Full article
(This article belongs to the Special Issue Laser Technology and Applications)
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12 pages, 2831 KiB  
Article
IKZF1 Variants Predicted Poor Outcomes in Acute Myeloid Leukemia Patients with CEBPA bZIP In-Frame Mutations
by Shunjie Yu, Lijuan Hu, Yazhen Qin, Guorui Ruan, Yazhe Wang, Hao Jiang, Feifei Tang, Ting Zhao, Jinsong Jia, Jing Wang, Qiang Fu, Xiaohui Zhang, Lanping Xu, Yu Wang, Yuqian Sun, Yueyun Lai, Hongxia Shi, Xiaojun Huang and Qian Jiang
Cancers 2025, 17(15), 2494; https://doi.org/10.3390/cancers17152494 - 29 Jul 2025
Viewed by 313
Abstract
Background: CCAAT/enhancer-binding protein alpha–basic leucine zipper in-frame (CEBPAbZIP-inf) mutations are associated with favorable outcomes in acute myeloid leukemia (AML). So far, there are limited data on integrating clinical and genomic features impacting the outcomes. Methods: Clinical and genomic data from [...] Read more.
Background: CCAAT/enhancer-binding protein alpha–basic leucine zipper in-frame (CEBPAbZIP-inf) mutations are associated with favorable outcomes in acute myeloid leukemia (AML). So far, there are limited data on integrating clinical and genomic features impacting the outcomes. Methods: Clinical and genomic data from consecutive patients with CEBPAbZIP-inf were reviewed. A Cox proportional hazards regression was used to identify the variables associated with event-free survival (EFS), relapse-free survival (RFS) and survival. Results: 224 CEBPAbZIP-inf patients were included in this study. In the 201 patients, except for the 19 receiving the transplant in the first complete remission with no events (the transplant cohort), multivariate analyses showed that IKZF1 mutations/deletions were significantly associated with poor EFS (p = 0.001) and RFS (p < 0.001); FLT3-ITD mutations, poor RFS (p = 0.048). In addition, increasing WBC count, lower hemoglobin concentration, non-intensive induction, and MRD positivity after first consolidation predicted poor outcomes. On the basis of the number of adverse prognostic covariates for RFS, the 201 patients were classified into low-, intermediate- or high-risk subgroups, and there were significant differences in the 3-year EFS, RFS and survival rates (all p < 0.001); however, except for survival in the low-risk group, these metrics were lower than those in the transplant cohort. Conclusions: We identified a potential high-risk population with adverse prognostic factors in CEBPAbZIP-inf AML patients for which transplantation should be considered. Full article
(This article belongs to the Section Cancer Survivorship and Quality of Life)
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26 pages, 4687 KiB  
Article
Geant4-Based Logging-While-Drilling Gamma Gas Detection for Quantitative Inversion of Downhole Gas Content
by Xingming Wang, Xiangyu Wang, Qiaozhu Wang, Yuanyuan Yang, Xiong Han, Zhipeng Xu and Luqing Li
Processes 2025, 13(8), 2392; https://doi.org/10.3390/pr13082392 - 28 Jul 2025
Viewed by 328
Abstract
Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for [...] Read more.
Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 106 Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm3) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R2 = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. Full article
(This article belongs to the Section Chemical Processes and Systems)
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28 pages, 3228 KiB  
Article
Examination of Eye-Tracking, Head-Gaze, and Controller-Based Ray-Casting in TMT-VR: Performance and Usability Across Adulthood
by Panagiotis Kourtesis, Evgenia Giatzoglou, Panagiotis Vorias, Katerina Alkisti Gounari, Eleni Orfanidou and Chrysanthi Nega
Multimodal Technol. Interact. 2025, 9(8), 76; https://doi.org/10.3390/mti9080076 - 25 Jul 2025
Viewed by 391
Abstract
Virtual reality (VR) can enrich neuropsychological testing, yet the ergonomic trade-offs of its input modes remain under-examined. Seventy-seven healthy volunteers—young (19–29 y) and middle-aged (35–56 y)—completed a VR Trail Making Test with three pointing methods: eye-tracking, head-gaze, and a six-degree-of-freedom hand controller. Completion [...] Read more.
Virtual reality (VR) can enrich neuropsychological testing, yet the ergonomic trade-offs of its input modes remain under-examined. Seventy-seven healthy volunteers—young (19–29 y) and middle-aged (35–56 y)—completed a VR Trail Making Test with three pointing methods: eye-tracking, head-gaze, and a six-degree-of-freedom hand controller. Completion time, spatial accuracy, and error counts for the simple (Trail A) and alternating (Trail B) sequences were analysed in 3 × 2 × 2 mixed-model ANOVAs; post-trial scales captured usability (SUS), user experience (UEQ-S), and acceptability. Age dominated behaviour: younger adults were reliably faster, more precise, and less error-prone. Against this backdrop, input modality mattered. Eye-tracking yielded the best spatial accuracy and shortened Trail A time relative to manual control; head-gaze matched eye-tracking on Trail A speed and became the quickest, least error-prone option on Trail B. Controllers lagged on every metric. Subjective ratings were high across the board, with only a small usability dip in middle-aged low-gamers. Overall, gaze-based ray-casting clearly outperformed manual pointing, but optimal choice depended on task demands: eye-tracking maximised spatial precision, whereas head-gaze offered calibration-free enhanced speed and error-avoidance under heavier cognitive load. TMT-VR appears to be accurate, engaging, and ergonomically adaptable assessment, yet it requires age-specific–stratified norms. Full article
(This article belongs to the Special Issue 3D User Interfaces and Virtual Reality—2nd Edition)
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22 pages, 4611 KiB  
Article
MMC-YOLO: A Lightweight Model for Real-Time Detection of Geometric Symmetry-Breaking Defects in Wind Turbine Blades
by Caiye Liu, Chao Zhang, Xinyu Ge, Xunmeng An and Nan Xue
Symmetry 2025, 17(8), 1183; https://doi.org/10.3390/sym17081183 - 24 Jul 2025
Viewed by 322
Abstract
Performance degradation of wind turbine blades often stems from geometric asymmetry induced by damage. Existing methods for assessing damage face challenges in balancing accuracy and efficiency due to their limited ability to capture fine-grained geometric asymmetries associated with multi-scale damage under complex background [...] Read more.
Performance degradation of wind turbine blades often stems from geometric asymmetry induced by damage. Existing methods for assessing damage face challenges in balancing accuracy and efficiency due to their limited ability to capture fine-grained geometric asymmetries associated with multi-scale damage under complex background interference. To address this, based on the high-speed detection model YOLOv10-N, this paper proposes a novel detection model named MMC-YOLO. First, the Multi-Scale Perception Gated Convolution (MSGConv) Module was designed, which constructs a full-scale receptive field through multi-branch fusion and channel rearrangement to enhance the extraction of geometric asymmetry features. Second, the Multi-Scale Enhanced Feature Pyramid Network (MSEFPN) was developed, integrating dynamic path aggregation and an SENetv2 attention mechanism to suppress background interference and amplify damage response. Finally, the Channel-Compensated Filtering (CCF) module was constructed to preserve critical channel information using a dynamic buffering mechanism. Evaluated on a dataset of 4818 wind turbine blade damage images, MMC-YOLO achieves an 82.4% mAP [0.5:0.95], representing a 4.4% improvement over the baseline YOLOv10-N model, and a 91.1% recall rate, an 8.7% increase, while maintaining a lightweight parameter count of 4.2 million. This framework significantly enhances geometric asymmetry defect detection accuracy while ensuring real-time performance, meeting engineering requirements for high efficiency and precision. Full article
(This article belongs to the Special Issue Symmetry and Its Applications in Image Processing)
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18 pages, 4044 KiB  
Article
Preparation and Immunogenicity Evaluation of a Ferritin-Based GnRH Nanoparticle Vaccine
by Ying Xu, Weihao Zhao, Yuhan Zhu, Bo Sun, Congmei Wu and Yuhe Yin
Vaccines 2025, 13(8), 781; https://doi.org/10.3390/vaccines13080781 - 23 Jul 2025
Viewed by 336
Abstract
Objectives: Research on the immunocastration vaccine is of great significance for animal management. In this study, the gonadotropin-releasing hormone (GnRH) ferritin nanoparticle vaccine was constructed using Spy Catcher-Spy Tag (SC-ST) as a delivery system; Methods: The Spy Catcher was constructed to [...] Read more.
Objectives: Research on the immunocastration vaccine is of great significance for animal management. In this study, the gonadotropin-releasing hormone (GnRH) ferritin nanoparticle vaccine was constructed using Spy Catcher-Spy Tag (SC-ST) as a delivery system; Methods: The Spy Catcher was constructed to fuse with the expression vector pET-30a-SF of ferritin nanoparticles. Two polypeptides, STG1: Spy Tag-GnRH I-PADRE and STG2: Spy Tag-GnRH I-GnRH II, coupled to SF in vitro to form two nanoparticles, were designed and synthesized to detect castration effects in mice. We mixed them with the adjuvant MONTANIDE ISA 206 VG to explore the adjuvant’s effect on immunogenicity; Results: All immunized groups produced anti-GnRH specific antibodies after the second immunization, which was significantly higher in the immunized group and the combined adjuvant group than in the control group, and the immune response could still be detected at the 12th week. The concentrations of testosterone, follicle-stimulating hormone, and luteinizing hormone in serum were significantly decreased. The number of sperm in the epididymis of mice in each immune group was significantly reduced, and the rate of sperm deformity was high; Conclusions: The two ferritin-based GnRH nanoparticles developed in this study can significantly cause testicular atrophy, decreased gonadal hormone concentration, decreased sperm count, and increased deformity rate in male mice. These findings provide experimental evidence supporting their potential application in animal immunocastration. Full article
(This article belongs to the Section Veterinary Vaccines)
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25 pages, 6462 KiB  
Article
Phenotypic Trait Acquisition Method for Tomato Plants Based on RGB-D SLAM
by Penggang Wang, Yuejun He, Jiguang Zhang, Jiandong Liu, Ran Chen and Xiang Zhuang
Agriculture 2025, 15(15), 1574; https://doi.org/10.3390/agriculture15151574 - 22 Jul 2025
Viewed by 202
Abstract
The acquisition of plant phenotypic traits is essential for selecting superior varieties, improving crop yield, and supporting precision agriculture and agricultural decision-making. Therefore, it plays a significant role in modern agriculture and plant science research. Traditional manual measurements of phenotypic traits are labor-intensive [...] Read more.
The acquisition of plant phenotypic traits is essential for selecting superior varieties, improving crop yield, and supporting precision agriculture and agricultural decision-making. Therefore, it plays a significant role in modern agriculture and plant science research. Traditional manual measurements of phenotypic traits are labor-intensive and inefficient. In contrast, combining 3D reconstruction technologies with autonomous vehicles enables more intuitive and efficient trait acquisition. This study proposes a 3D semantic reconstruction system based on an improved ORB-SLAM3 framework, which is mounted on an unmanned vehicle to acquire phenotypic traits in tomato cultivation scenarios. The vehicle is also equipped with the A * algorithm for autonomous navigation. To enhance the semantic representation of the point cloud map, we integrate the BiSeNetV2 network into the ORB-SLAM3 system as a semantic segmentation module. Furthermore, a two-stage filtering strategy is employed to remove outliers and improve the map accuracy, and OctoMap is adopted to store the point cloud data, significantly reducing the memory consumption. A spherical fitting method is applied to estimate the number of tomato fruits. The experimental results demonstrate that BiSeNetV2 achieves a mean intersection over union (mIoU) of 95.37% and a frame rate of 61.98 FPS on the tomato dataset, enabling real-time segmentation. The use of OctoMap reduces the memory consumption by an average of 96.70%. The relative errors when predicting the plant height, canopy width, and volume are 3.86%, 14.34%, and 27.14%, respectively, while the errors concerning the fruit count and fruit volume are 14.36% and 14.25%. Localization experiments on a field dataset show that the proposed system achieves a mean absolute trajectory error (mATE) of 0.16 m and a root mean square error (RMSE) of 0.21 m, indicating high localization accuracy. Therefore, the proposed system can accurately acquire the phenotypic traits of tomato plants, providing data support for precision agriculture and agricultural decision-making. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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16 pages, 2084 KiB  
Article
Accelerometer Measurements: A Learning Tool to Help Older Adults Understand the Importance of Soft-Landing Techniques in a Community Walking Class
by Tatsuo Doi, Ryosuke Haruna, Naoyo Kamioka, Shuzo Bonkohara and Nobuko Hongu
Sensors 2025, 25(15), 4546; https://doi.org/10.3390/s25154546 - 22 Jul 2025
Viewed by 214
Abstract
When people overextend their step length, it leads to an increase in vertical movement and braking force. The overextension elevates landing impacts, which may increase pain in the knees or lower back. The objective of this study was to examine the effects of [...] Read more.
When people overextend their step length, it leads to an increase in vertical movement and braking force. The overextension elevates landing impacts, which may increase pain in the knees or lower back. The objective of this study was to examine the effects of soft-landing walking techniques in a 90 min, instructor-led group class for older adults. To evaluate a landing impact, an accelerometer measurement system (Descente LTD., Tokyo, Japan) was used to measure a participant 10 meter (m) of walking. Assessment outcomes included the average number of steps, step length, upward acceleration which reflects the landing impact, and survey questions. A total of 223 older adults (31 men, 192 women, mean age 74.4 ± 5.7 years) completed the walking lesson. Following the lesson, participants decreased their step lengths and reduced upward acceleration, along with an increased step count. The number of steps increased, and a positive correlation (r = 0.73, p < 0.01) was observed between the rate of change in step length and upward acceleration. Over 95% of participants gave high marks for practicality and understanding the accelerometer measurements. The information derived from this study will provide valuable insight into the effectiveness of soft-landing techniques as a promotion of a healthy walking program for older adults. Full article
(This article belongs to the Special Issue Advanced Sensors for Health Monitoring in Older Adults)
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