Search Results (18)

Background: The timing of perceived exertion assessment within a resistance training set may influence physiological adaptation; this study aimed to directly compare first-repetition (RPE-1) and last-repetition (RPE-last) monitoring strategies applied to functional, neuromuscular, body composition, and cardiometabolic adaptations during elastic band resistance training in older adults. Methods: Forty sedentary older adults (n = twenty per group, sex-balanced) were randomly assigned to RPE-1 or RPE-last using the OMNI-RES elastic band scale during a 16-week, open-label high-intensity elastic band resistance training program, with blinded outcome assessment and data analysis; three withdrew during the intervention (RPE-1, n = 2; RPE-last, n = 1), and all forty randomized participants were retained in the intention-to-treat analysis via baseline carry-forward. Functional capacity, isokinetic knee and elbow strength (60 and 180°/s), DXA-assessed body composition, and fasting cardiometabolic biomarkers were evaluated pre- and post-intervention. Between-group effects were analyzed using ANCOVA with Benjamini–Hochberg false discovery rate (FDR) correction, whereas functional equivalence was assessed using Welch-corrected two one-sided tests (TOST) with percentile bootstrap against published minimum clinically important difference (MCID) margins. Results: Both groups improved significantly across most outcomes. For the primary outcomes, RPE-1 was associated with greater improvements across all eight isokinetic strength conditions after FDR correction, with adjusted between-group differences ranging from +7.86 to +18.38 Nm in favor of RPE-1, and all 95% CIs excluding zero (ηp² = 0.14–0.37). The Welch-corrected TOST demonstrated functional equivalence between monitoring strategies for all four functional outcomes in the primary intention-to-treat analysis. However, the pre-specified complete-case sensitivity did not confirm equivalence in the 30-Second Chair Stand Test, indicating that this outcome was sensitive to the handling of missing data. Among secondary exploratory outcomes, RPE-1 also was associated with greater improvements in DXA-assessed fat mass (adjusted difference—−1.33 kg; 95% CI—−2.02 to −0.63; ηp² = 0.29) and lean mass (+0.42 kg; 95% CI—0.02 to 0.83; ηp² = 0.11), high-density lipoprotein cholesterol (+5.11 mg/dL; 95% CI—1.52 to 8.69; ηp² = 0.18), and low-density lipoprotein cholesterol (−16.99 mg/dL; 95% CI—−23.22 to −10.76; ηp² = 0.45). Conclusion: RPE-1 appeared to confer greater benefits for maximizing neuromuscular strength, whereas RPE-last provides an equally valid alternative when preserving physical independence is the primary objective. Full article

Satellite remote sensing imagery has become an essential resource for large-scale surface water monitoring. Nevertheless, in karst regions, the elongated and fragmented morphology of water bodies, along with terrain shadows and vegetation interference, still leads to limitations in existing methods for small water body detection and accurate boundary delineation. To overcome the aforementioned issues, this paper proposes MESA-Net, a CNN–Mamba hybrid segmentation network for water body extraction in complex karst terrain. The network employs ResNet-18 as an encoder to extract shallow-level features. The decoder primarily consists of three modules: the Cross-Scale Adaptive Feature Fusion (CAFF) module, the Directional Gradient Histogram Edge-Guided Fusion (DGHEF) module, and the Omni-directional Global-Local Mamba Block (OGLMB). Among these, the CAFF module enhances the detection capability for small-scale water bodies by performing cross-scale feature fusion and dynamic weight allocation on the feature outputs from each level of the encoder. The OGLMB integrates an omnidirectional state space model with an 8-directional scanning mechanism and cross-attention guidance, effectively enhancing the ability to represent the structural continuity and global consistency of water bodies. The DGHEF utilizes directional gradient histograms to explicitly model multi-directional boundary information of water bodies, and combines this with a boundary guidance mechanism to enhance the representation of water body boundary features whilst suppressing spurious responses. In addition, the LJ-Water dataset has been constructed for the Lijiang River Basin in Guangxi, which is based on Sentinel-2 imagery. To validate the effectiveness and generalization capability of the method, comparative experiments were conducted on the self-built LJ-Water dataset as well as the publicly available Water-CD and LoveDA datasets. Experimental results demonstrate that MESA-Net consistently outperforms representative CNN-based, Transformer-based, and Mamba-based segmentation networks. On the LJ-Water dataset, it achieves 84.59% IoU and 91.65% F1, whilst on the Water-CD dataset, it attains 92.15% IoU and 95.91% F1, and 69.83% IoU and 82.24% F1 on the LoveDA dataset. Relative to the strongest baseline method, the proposed model achieved IoU gains of 1.51%, 2.34%, and 1.73% on the three datasets, respectively. In summary, MESA-Net demonstrates superior water segmentation performance under complex background conditions. Full article

High-intensity interval training (HIIT) provides substantial cardiovascular benefits; however, precise monitoring typically requires expensive devices. These systems are feasible in research laboratories but are costly for schools and the fitness industry. Low-cost, validated devices are required to facilitate broader implementation. A cross-sectional study was conducted with 213 students (173 men and 40 women) from the Catholic University of Valencia, Spain. The participants completed an HIIT protocol consisting of five 3 min blocks. Heart rate (HR) was recorded using a Moofit HW401 armband (ANT+ technology). Ratings of perceived exertion (RPE, Omni-Res scale) and the Wint index were also obtained. Pearson correlations were computed between reserve heart rate (HRr), RPE, and Wint index during the warm-up phases (T1, T2) and HIIT, stratified by sex, age, and body mass index (BMI). HRr was strongly correlated with the Wint index (r = 0.95, p < 0.0001) and moderately correlated with RPE (r = 0.235, p = 0.001). No significant sex differences were observed (men 83.66 ± 8.18% vs. women 82.31 ± 10.89%; p > 0.05). Correlations were weaker in participants with extreme BMI values (n < 10, obese). The Moofit HW401 armband showed consistent agreement between HRr, RPE, and Wint index during HIIT, supporting its practical use for group monitoring in educational settings, pending formal validation against gold standards. Full article

Objectives: To examine three elastic band resistance training (EB-RT) modalities—high-velocity (HVRT), accentuated eccentric (Aecc), and maximal strength (Max)—on bone health, strength, redox-inflammatory profile, and neuroplasticity in sedentary older adults. Methods: Sixty-one participants (69.41 ± 4.61 years) were randomly assigned to HVRT (n = 21), Aecc (n = 13), Max (n = 10), or passive controls (n = 17). Training was conducted three times a week for 16 weeks. Sessions included four sets of alternating upper- and lower-limb EB exercises, with intensity guided by the OMNI–RES EB scale. HVRT emphasized explosive concentric actions [~70% one-repetition maximum (1RM); 3–4 rating of perceived exertion in the first repetition (RPE-1)]. Aecc performed 5 s eccentric overload [>100% 1RM; 7–8 RPE-1]. Max employed controlled 2 s concentric/eccentric actions [~80–85% 1RM; 7–8 RPE-1]. Results: All training groups improved isokinetic strength (p < 0.01, g = 0.91–2.40). HVRT increased brain-derived neurotrophic factor (BDNF) (p = 0.019, g = 0.42) and glutathione peroxidase (GPx) (p < 0.001, g = 0.31). Aecc elicited the strongest osteoanabolic and antioxidant effects (P1NP, p = 0.001, g = 1.21; β-CTX, p < 0.001, g = 1.82; F2-isoprostanes, p = 0.007, g = 0.94). Max induced moderate bone turnover benefits (P1NP, p = 0.005, g = 1.08; β-CTX, p < 0.001, g = 1.12), but no GPx or BDNF gains. Controls maintained or declined all variables. Conclusions: EB-RT over 16 weeks improved most outcomes overall, showing modality-specific trends: HVRT favored neuroplasticity, Aecc enhanced redox-inflammatory and bone remodeling responses, and Max improved strength and bone health. These findings support elastic band resistance training as a safe and individualized strategy for healthy aging. Full article

With rising demand for cross-camera person re-identification (ReID) in smart cities, low-resolution (LR) images severely hinder practical ReID performance due to detail loss and weakened identity features. This paper proposes two solutions to address this bottleneck: (1) super-resolution (SR) techniques, including hybrid attention transformer (HAT), pixel-level and semantic-level adjustable SR (PiSA-SR), and omni aggregation networks for lightweight image SR (Omni-SR), are used to enhance image visual quality, and the enhanced images are applied to three ReID methods, including semantically controllable self-supervised learning framework-REID (SOLIDER-REID), light-REID, and relation-aware global attention (RGA), for performance assessment. (2) An end-to-end framework integrating HAT and SOLIDER-REID is designed, in which HAT enhances LR images via multi-scale attention to restore discriminative details, while SOLIDER-REID’s semantic controller suppresses background noise to focus on the pedestrian regions. Extensive experiments on the Market-1501 dataset show that the first solution slightly improves ReID accuracy, e.g., PiSA-SR + SOLIDER-REID achieves 92.0% mAP, 0.4% higher than SOLIDER-REID alone, while slightly sacrificing speed. The second solution significantly boosts LR ReID performance at the cost of a certain increase in time. For LR images, even 32 × 32 images, HAT-SOLIDER achieves 59.8% mAP and 80.4% Rank-1, 18.5% higher in mAP and 19.2% higher in Rank-1 than SOLIDER-REID alone. This work provides effective solutions for LR-induced performance degradation in cross-camera ReID. Full article

With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further complicates accurate assessment of fruit maturity. To address these challenges, this study proposes an improved model based on YOLOv8, named ORD-YOLO, for citrus fruit maturity detection. To enhance the model’s robustness in complex environments, several key improvements have been introduced. First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. Second, the feature fusion process is optimized and inference speed is increased by integrating a Re-parameterizable Generalized Feature Pyramid Network (RepGFPN). Third, the detection head is redesigned using a Dynamic Head structure that leverages dynamic attention mechanisms to enhance key feature perception. Additionally, the loss function is optimized using InnerDIoU to improve object localization accuracy. Experimental results demonstrate that the enhanced ORD-YOLO model achieves a precision of 93.83%, a recall of 91.62%, and a mean Average Precision (mAP) of 96.92%, representing improvements of 4.66%, 3.3%, and 3%, respectively, over the original YOLOv8 model. ORD-YOLO not only maintains stable and accurate citrus fruit maturity recognition under complex backgrounds, but also significantly reduces misjudgment caused by manual assessments. Furthermore, the model enables real-time, non-destructive detection. When deployed on harvesting robots, it can substantially increase picking efficiency and reduce post-maturity fruit rot due to delayed harvesting. These advancements contribute meaningfully to the quality improvement, efficiency enhancement, and digital transformation of the citrus industry. Full article

Background: Warm-up strategies are essential for optimizing strength-training performance. Traditional warm-ups improve neuromuscular readiness, whereas post-activation potentiation (PAP) has been proposed to acutely enhance muscular output. This randomized crossover study compared the acute effects of traditional and PAP-based warm-ups on local muscular endurance (LME) during free weight back squats in resistance-trained men. Methods: Twelve trained males (age: 41.3 ± 5.7 years; one repetition maximum squat: 129.3 ± 14.3 kg) completed three randomized squat sessions: mobility with LME (M + LME), traditional warm-up with LME (T + LME), and PAP with LME (PAP + LME). The sessions were spaced 48 h apart. Outcomes included the number of repetitions, blood lactate concentration, time under tension (TUT), perceived exertion through OMNI Resistance Exercise Scale (OMNI-RES), and pain perception through visual analogue scale (VAS). One-way ANOVA and partial eta-squared (η²p) were used for statistical analyses. Results: PAP + LME significantly increased the number of repetitions (15.63 ± 3.66) compared to both M + LME (12.38 ± 3.89) and T + LME (13.63 ± 3.82; p < 0.0001). Blood lactate levels were significantly higher in PAP + LME (8.98 ± 3.87 mmol/L) compared to M + LME (5.08 ± 0.97 mmol/L; p = 0.01). TUT was significantly shorter in both the PAP + LME and T + LME groups than in the M + LME group (p < 0.05). VAS scores were higher after PAP + LME (8.50 ± 0.45) than after M + LME (6.50 ± 1.20; p = 0.02), while OMNI-RES scores did not differ significantly between the protocols. Conclusions: Both traditional and PAP-based warm-ups improved squat LME compared with mobility alone. PAP elicited greater repetition performance and metabolic stress but also increased discomfort. Warm-up selection should align with training goals, balancing performance benefits and perceived fatigue. Full article

Objectives: This study aimed to compare the acute responses of the muscular activity of primary movers during bench press execution under asymmetric loads (25%, 50%, and 75%). Methods: The study included 30 resistance-trained males (n = 25, age = 22.73 ± 3.44 years, height= 1.77 ± 0.06 m, body mass= 76.77 ± 9.28 kg) and females (n = 5, age = 22.5 ± 1.19 years, height = 1.63 ± 0.04 m, body mass = 56.78 ± 2.90 kg). We assessed the two portions of the dominant pectoralis major, triceps brachii, anterior deltoid, and both external oblique peak activities (sEMG) during concentric and eccentric phases. We performed a repeated-measures design to establish the differences between muscle activity, barbell center of mass acceleration, and OMNI-Perceived Exertion Scale for Resistance Exercise (OMNI-RES) in a bench press under seven different conditions. Results: The linear mixed model showed a significant fixed effect for exercise condition for muscles (p < 0.001) in the concentric and eccentric phases. We found significantly higher clavicularis (d = 0.54; d = 1.15) and sternalis (d = 0.38; d = 0.86) pectoralis major activation of the dominant side under high (50% and 75%), non-dominant-side, de-loaded conditions in the eccentric phase (p < 0.001), with large effects. Contralateral core muscles (external oblique) of the dominant and non-dominant sides were significantly (p < 0.001) highly activated under all asymmetric conditions in the concentric phase (from d = 0.89 to d = 2.30). Conclusions: The asymmetric load bench press provoked a higher pectoralis major activation on the loaded side when de-loading the other side. The contralateral external oblique doubles the muscle activity in the most asymmetric conditions. Full article

Background: One of the most significant challenges for exercise professionals in designing strength training programs is determining the intensity or effort level of each set performed. One of the most studied methodologies has been the use of Rate of Perceived Exertion (RPE) scales. This study aims to analyze the application of the OMNI-RES scale for monitoring training intensity across different relative loads and fatigue levels in various training protocols. Methods: In this cross-sectional study, participants completed nine exercise sessions, with one week separating each session. The first session involved a one-repetition maximum (1RM) test in the bench press (BP) to identify the load–velocity relationship. Subsequently, each participant randomly performed two maximum repetition (MNR) protocols at 60% and 90% of 1RM, and two protocols with a 30% velocity loss (VL) at 60% of 1RM and a 10% VL at 90% of 1RM. These sessions were repeated one week later. Results: significant differences were found between the four bench press protocols regarding the number of repetitions and the percentage of velocity loss per set (p < 0.001). However, the RPE of the MNR protocol at 60% of 1RM was significantly higher than the other protocols. Moreover, the RPE for the protocol at 60% of 1RM with a 30% VL was similar to that at 90% of 1RM with a 10% VL (p = 1.000). Post-exercise blood lactate concentrations, percentage VL at 1 m·s⁻¹, and the effort index were significantly higher in the MNR protocol at 60% of 1RM compared to all other protocols (p < 0.001). Conclusions: The most important finding of this study is that the OMNI-RES scale may not be a reliable indicator of exercise intensity. This is because the highest values on the scale were observed at the lowest relative intensity (60% 1RM) during the maximum number of repetitions (MNR) protocol, corresponding to the maximum volume. Full article

Training using instability devices is common; however, for highly trained athletes, a single device may not provide sufficient challenge. This study examines the effect of superimposed vibration in suspended kneeling rollout. Seventeen physically active participants performed the exercise with non-vibration, vibration at 25 Hz, and vibration at 40 Hz. Muscle activation of the pectoralis clavicularis, pectoralis sternalis, anterior deltoid, serratus anterior, infraspinatus, and latissimus dorsi was recorded during exercise, and the perception of effort was recorded after exercise (OMNI-Res scale). One-way repeated-measures analysis of variance (ANOVA) showed significant differences for the kneeling rollout (p < 0.05). Friedman’s test showed significant differences in the OMNI-Res (p = 0.003). Pairwise comparison showed significant differences in the anterior deltoid (p = 0.004), latissimus dorsi (p < 0.001), infraspinatus (p = 0.001), and global activity (p < 0.001) between the 25 Hz and non-vibration conditions. It also showed significant differences between the 40 Hz and non-vibration conditions for pectoralis sternalis (p = 0.021), anterior deltoid (p = 0.005), latissimus dorsi (p < 0.001), infraspinatus (p = 0.027), and global activity (p < 0.001). The post hoc Conover pairwise comparison showed significant differences in the OMNI-Res only between the non-vibration and vibration at 40 Hz conditions (p = 0.011). Superimposed vibration increases the muscle activation of the upper limbs when performing the suspended kneeling rollout. Full article

Background/Objectives: The role of artificial intelligence (AI) in radiological image analysis is rapidly evolving. This study evaluates the diagnostic performance of Chat Generative Pre-trained Transformer Omni (GPT-4 Omni) in detecting intracranial hemorrhages (ICHs) in non-contrast computed tomography (NCCT) images, along with its ability to classify hemorrhage type, stage, anatomical location, and associated findings. Methods: A retrospective study was conducted using 240 cases, comprising 120 ICH cases and 120 controls with normal findings. Five consecutive NCCT slices per case were selected by radiologists and analyzed by ChatGPT-4o using a standardized prompt with nine questions. Diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated by comparing the model’s results with radiologists’ assessments (the gold standard). After a two-week interval, the same dataset was re-evaluated to assess intra-observer reliability and consistency. Results: ChatGPT-4o achieved 100% accuracy in identifying imaging modality type. For ICH detection, the model demonstrated a diagnostic accuracy of 68.3%, sensitivity of 79.2%, specificity of 57.5%, PPV of 65.1%, and NPV of 73.4%. It correctly classified 34.0% of hemorrhage types and 7.3% of localizations. All ICH-positive cases were identified as acute phase (100%). In the second evaluation, diagnostic accuracy improved to 73.3%, with a sensitivity of 86.7% and a specificity of 60%. The Cohen’s Kappa coefficient for intra-observer agreement in ICH detection indicated moderate agreement (κ = 0.469). Conclusions: ChatGPT-4o shows promise in identifying imaging modalities and ICH presence but demonstrates limitations in localization and hemorrhage type classification. These findings highlight its potential for improvement through targeted training for medical applications. Full article

Due to COVID-19, online education has become an important tool for teachers to teach students. Also, teachers depend on a multimedia teaching system (platform) to finish online education. However, interacted images from a multimedia teaching system may suffer from noise. To address this issue, we propose a lightweight image denoising network (LIDNet) for multimedia teaching systems. A parallel network can be used to mine complementary information. To achieve an adaptive CNN, an omni-dimensional dynamic convolution fused into an upper network can automatically adjust parameters to achieve a robust CNN, according to different input noisy images. That also enlarges the difference in network architecture, which can improve the denoising effect. To refine obtained structural information, a serial network is set behind a parallel network. To extract more salient information, an adaptively parametric rectifier linear unit composed of an attention mechanism and a ReLU is used into LIDNet. Experiments show that our proposed method is effective in image denoising, which can also provide assistance for multimedia teaching systems. Full article

This study investigated consumer behaviors in conspicuous omni-signaling—its internal motivations and its consequences on social needs fulfilment and re-consumption intention in the context of luxury fashion. A phenomenon of conspicuous consumption is identified with the consumption and display of conspicuous goods to signal status, wealth, and prestige. Digital development has made conspicuous signaling radically emerge in social media through the posting of photos, videos, or stories of luxury goods. This drives an emerging phenomenon of conspicuous omni-signaling, the use of both offline and online media to signal conspicuous consumption hybridlike. As a new phenomenon, little is known of consumer behaviors related to conspicuous omni-signaling. To facilitate the investigation, an online survey was conducted to collect data from 474 valid respondents across eight cities representing various conspicuous consumption characteristics of Indonesian consumers. Veblen’s conspicuous consumption and Maslow’s hierarchy of needs theories were employed as the main lens for analysis. PLS-SEM technique was employed as the research model uses mixed reflective and formative constructs. WarpPLS 7.0 was then used for data analysis. The results indicated that luxury values and fashion consciousness positively affect conspicuous omni-signaling. This study also found that conspicuous omni-signaling affects conspicuous re-consumption both directly and indirectly through social needs fulfilment. This study contributes to extend the concept of conspicuous offline consumption and conspicuous online consumption to conspicuous omni-signaling. This study also confirms conflicting results in the effect of conspicuous consumption on social needs fulfilment, and conflicting results in the effect of conspicuous consumption on conspicuous re-consumption. Full article

The number of wheat ears in a field is an important parameter for accurately estimating wheat yield. In a large field, however, it is hard to conduct an automated and accurate counting of wheat ears because of their density and mutual overlay. Unlike the majority of the studies conducted on deep learning-based methods that usually count wheat ears via a collection of static images, this paper proposes a counting method based directly on a UAV video multi-objective tracking method and better counting efficiency results. Firstly, we optimized the YOLOv7 model because the basis of the multi-target tracking algorithm is target detection. Simultaneously, the omni-dimensional dynamic convolution (ODConv) design was applied to the network structure to significantly improve the feature-extraction capability of the model, strengthen the interaction between dimensions, and improve the performance of the detection model. Furthermore, the global context network (GCNet) and coordinate attention (CA) mechanisms were adopted in the backbone network to implement the effective utilization of wheat features. Secondly, this study improved the DeepSort multi-objective tracking algorithm by replacing the DeepSort feature extractor with a modified ResNet network structure to achieve a better extraction of wheat-ear-feature information, and the constructed dataset was then trained for the re-identification of wheat ears. Finally, the improved DeepSort algorithm was used to calculate the number of different IDs that appear in the video, and an improved method based on YOLOv7 and DeepSort algorithms was then created to calculate the number of wheat ears in large fields. The results show that the mean average precision (mAP) of the improved YOLOv7 detection model is 2.5% higher than that of the original YOLOv7 model, reaching 96.2%. The multiple-object tracking accuracy (MOTA) of the improved YOLOv7–DeepSort model reached 75.4%. By verifying the number of wheat ears captured by the UAV method, it can be determined that the average value of an L1 loss is 4.2 and the accuracy rate is between 95 and 98%; thus, detection and tracking methods can be effectively performed, and the efficient counting of wheat ears can be achieved according to the ID value in the video. Full article

In the upcoming 6G era, edge artificial intelligence (AI), as a key technology, will be able to deliver AI processes anytime and anywhere by the deploying of AI models on edge devices. As a hot issue in public safety, person re-identification (Re-ID) also needs its models to be urgently deployed on edge devices to realize real-time and accurate recognition. However, due to complex scenarios and other practical reasons, the performance of the re-identification model is poor in practice. This is especially the case in public places, where most people have similar characteristics, and there are environmental differences, as well other such characteristics that cause problems for identification, and which make it difficult to search for suspicious persons. Therefore, a novel end-to-end suspicious person re-identification framework deployed on edge devices that focuses on real public scenarios is proposed in this paper. In our framework, the video data are cut images and are input into the You only look once (YOLOv5) detector to obtain the pedestrian position information. An omni-scale network (OSNet) is applied through which to conduct the pedestrian attribute recognition and re-identification. Broad learning systems (BLSs) and cycle-consistent adversarial networks (CycleGAN) are used to remove the noise data and unify the style of some of the data obtained under different shooting environments, thus improving the re-identification model performance. In addition, a real-world dataset of the railway station and actual problem requirements are provided as our experimental targets. The HUAWEI Atlas 500 was used as the edge equipment for the testing phase. The experimental results indicate that our framework is effective and lightweight, can be deployed on edge devices, and it can be applied for suspicious person re-identification in public places. Full article