Search Results (12,812)

The post-peak failure and softening mechanisms of surrounding rock in common tunnel, mine shaft, and roadway engineering primarily include radial tensile softening, shear sliding softening, and circumferential compressive–shear softening. Given the distinct post-peak failure and softening mechanisms, the softening coefficient in self-similarity analytical algorithms for stability analysis should differ accordingly. In this paper, to address the limitation of the existing self-similarity numerical algorithms for the deformation and failure of rock surrounding circular excavations—which typically employ only the plastic shear strain as the softening coefficient—we extend the self-similarity numerical algorithm by incorporating two additional softening coefficients: the maximum and minimum plastic principal strain. We validated the extended algorithm’s accuracy and reliability by comparing its stress, displacement, and plastic zone radius predictions with those obtained through numerical simulation and engineering monitoring and examined its sensitivity to step length variations under various softening coefficients and yield criteria. According to the validation and comparison with existing algorithms, the extended algorithm extends the applicability scope of the original self-similarity numerical algorithm and significantly improves the accuracy of the calculated results. Finally, using the extended algorithm, we systematically compared and quantitatively analyzed the stress, deformation, and failure characteristics around a circular excavation across different softening coefficient categories, including their critical values, revealing the influence patterns of the softening coefficients and their critical values on the stability of engineering surrounding rock. Full article

The growth of e-commerce live streaming has expanded sales channel options for fresh agricultural suppliers. This study investigates a two-echelon supply chain consisting of a fresh agricultural supplier and downstream retailers. Using differential game theory, we examine the supplier’s preservation technology level and product greenness, analyzing and comparing equilibrium strategies under three different modes: e-commerce platform sales mode (SP), head streamer sales mode (SH) and ordinary streamer sales mode (SN). The results demonstrate that SP is the dominant strategy when retailers’ marginal profits are low. Conversely, under high marginal profit conditions, the optimal selection depends on streamer cooperation costs: SH is preferred with low head streamer costs; widening cost gaps introduce temporal considerations between SH and SN; further gap expansion makes SN optimal. Furthermore, product greenness is related to supplier’s marginal profit, while the preservation technology level is jointly determined by supplier’s marginal profit and retailers’ inspection costs. Finally, combinations of these modes are also investigated. Full article

Canadian real-world data (RWD) regarding palbociclib as a first-line therapy for patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2−) metastatic breast cancer (MBC) is limited. The PALbociclib CANadian (PALCAN) study examined palbociclib utilization patterns as first-line treatment for HR+/HER2− MBC using Alberta health administrative data. The final PALCAN cohort included 472 female patients with a median age of 64 years and a median follow-up time of 22.8 months (IQR: 0.7–88.2). The median (95% CI) duration of treatment was 13.8 (12.7–15.1) months in the overall cohort (IQR: 5.6, 24.8 months), and the probability of treatment discontinuation within the first year was 45%. Aromatase inhibitors (AIs) and fulvestrant were the accompanying endocrine therapies (ETs) in 83% (N = 393) and 14% (N = 64) (15 with unknown accompanying therapy) of patients, respectively. The median duration of treatment for patients receiving an AI as an accompanying therapy was 15.1 (13.6–17.4) months and 7.9 months (5.8–12.6) for patients receiving fulvestrant, which may suggest endocrine resistance in the latter group. The PALCAN data provides insights into practice patterns and the effectiveness of palbociclib as a first-line therapy in female patients with HR+/HER2− breast cancer in the Canadian real-world setting. Full article

Tetra-missing rib honeycombs (TMRHs), characterized by monoclinic geometry, exhibit high elastic stiffness but suffer from poor deformation stability and reduced axial load-bearing capacity, which limit their applicability in energy-absorbing and load-sensitive engineering structures. To address these inherent drawbacks, this study proposes two symmetry-enhanced tetra-missing rib honeycomb configurations through overall axisymmetric design and subunit-level symmetric optimization. A finite element model was established in Abaqus/Explicit and validated against quasi-static compression experiments, demonstrating good agreement in deformation modes and mechanical responses. Systematic numerical investigations were then conducted to compare the mechanical properties and deformation behaviors of three honeycomb layouts, including the conventional TMRH and the proposed symmetric designs. Furthermore, the effects of impact velocity on mechanical performance were examined to evaluate the dynamic response characteristics of the structures. Finally, the influence of subunit angle parameters on the stiffness, energy absorption, and deformation stability of the tetra-missing rib honeycombs was comprehensively analyzed. The results provide insight into the role of symmetry and geometric parameters in improving the mechanical performance of TMRH-based structures and offer guidance for the design of high-performance auxetic honeycombs. Full article

This work reports on the extraction and separation of Co(II) and Li(I) ions from chloride solutions using a synergistic mixture, namely Cyphos IL 101 (trihexyl(tetradecyl)phosphonium chloride[R₄PCl] and TBP (tributylphosphate). This system has not been described up to now. The aim of this research was to compare the extraction efficiency (%E) and the extraction selectivity of Co(II) over Li(I) (S_Co/Li) using single extractants and their equimolar mixture. Co(II) extraction with Cyphos IL 101 and TBP depends strongly on hydrochloric acid concentration in the aqueous phase. The separation coefficient of the studied metal ions was determined depending on the hydrochloric acid concentrations in the aqueous phase. The significance of the work is in the examination of the re-extraction of cobalt(II) from the organic phase after extraction. For this purpose, inorganic acids were investigated as the stripping agents, i.e., HCl (hydrochloric acid), H₂SO₄ (sulfuric acid) and HNO₃ (nitric acid). Finally, optimal conditions for the separation of Co(II) and Li(I) were established by using a synergistic mixture. A highly selective and effective solvent extraction of cobalt(II) over lithium(I) from 5 mol∙dm⁻³ hydrochloric acid has been achieved with the synergistic mixture of 0.1 mol∙dm⁻³ Cyphos IL 101 and 0.1 mol∙dm⁻³ TBP in kerosene. The selectivity coefficients of Co(II) over Li(I) (C_Co/Li) in the solvent extraction with 0.1 mol∙dm⁻³ Cyphos IL 101, 0.1 mol∙dm⁻³ TBP, and with their equimolar mixture were found to be equal: 73.1, 3.7 and 225.5, respectively. Efficient Co(II) stripping was achieved using 0.5 mol∙dm⁻³ sulfuric acid. Full article

Intelligent fruit-picking robots have emerged as a promising solution to labor shortages and the increasing costs of manual harvesting. This review provides a systematic and critical overview of recent advances in three core domains: (i) vision-based fruit and peduncle detection, (ii) motion planning and obstacle-aware navigation, and (iii) robotic manipulation technologies for diverse fruit types. We summarize the evolution of deep learning-based perception models, highlighting improvements in occlusion robustness, 3D localization accuracy, and real-time performance. Various planning frameworks—from classical search algorithms to optimization-driven and swarm-intelligent methods—are compared in terms of efficiency and adaptability in unstructured orchard environments. Developments in multi-DOF manipulators, soft and adaptive grippers, and end-effector control strategies are also examined. Despite these advances, critical challenges remain, including heavy dependence on large annotated datasets; sensitivity to illumination and foliage occlusion; limited generalization across fruit varieties; and the difficulty of integrating perception, planning, and manipulation into reliable field-ready systems. Finally, this review outlines emerging research trends such as lightweight multimodal networks, deformable-object manipulation, embodied intelligence, and system-level optimization, offering a forward-looking perspective for autonomous harvesting technologies. Full article

Ultra-high-molecular-weight polyethylene (UHMWPE) thin films are considered promising shielding materials against hypervelocity microparticle impacts in space environments. In this study, a finite element-smoothed particle hydrodynamics (FEM-SPH) adaptive coupling simulation method was developed to reveal the damage mechanisms of UHMWPE films impacted by alumina (Al₂O₃) particles with a diameter of 10 μm. A 100 μm thick single-layer UHMWPE film was subjected to normal impacts at velocities ranging from 1 to 30 km/s. The morphology and characteristics of craters formed on the film surface were analyzed, revealing the velocity-dependent transition from plastic deformation to complete perforation. At 10 km/s, additional oblique impact simulations at 30°, 45°, 60° and 75° were performed to assess the effect of impact angle on damage morphology. Furthermore, the damage evolution in double-layer UHMWPE films was examined under impact velocities of 5, 10, 15, 20 and 25 km/s, showing enhanced protective performance compared to single-layer films. Finally, the critical influence parameters for UHMWPE failure were discussed, providing criteria for evaluating the shielding limits. This work offers computational methods and predictive tools for assessing hypervelocity microparticle impact and contributes to the structural protection design of spacecraft operating in the harsh space environment. Full article

Hemostasis and thrombosis reflect a delicate balance, regulated by the interplay between procoagulant and anticoagulant mechanisms. Hemophilia is traditionally viewed as a bleeding disorder, but emerging evidence highlights the paradoxical risks of thrombosis in hemophilia patients. We explore the landscape of hemophilia management, emphasizing challenges of balancing hemostasis in the context of aging, novel non-factor replacement therapies (NRTs), and comorbidity-driven thrombotic complications. Therapeutic approaches, including innovative NRTs, such as emicizumab, or rebalancing agents (e.g., concizumab, marstacimab, fitusiran), offer promising advancements in bleeding prophylaxis but may increase thrombotic risks. Conversely, novel anticoagulants, such as FXI inhibitors, offer potential thrombosis protection with minimal bleeding risk. Our review examines the impact of aging-related comorbidities, including cardiovascular disease, atrial fibrillation, HIV-associated complications, and acute coronary syndromes, on thrombotic risk in hemophilia patients. Evidence-based strategies for balancing hemostasis and thrombosis are outlined alongside experimental models, thrombin generation assays, and advancements in rebalancing coagulation through natural anticoagulant modulation. FXI inhibition emerges as a paradigm shift in thrombosis management, offering reduced bleeding risks while preserving vascular health. Finally, this review highlights the need for global laboratory assays to personalize treatments, emphasizing strategies to optimize safety and efficacy, particularly as hemophilia patients live longer with complex comorbidity profiles. Full article

In contemporary education, schools are increasingly expected to foster students’ subjective well-being alongside academic achievement, as both are recognized as mutually reinforcing conditions for learning, success, and long-term outcomes. This study presents Appwel, a scalable instrument assessing pupils’ self-reported school experiences. It contains 21 statements rated on a 5-point Likert scale. Appwel was developed through a sequential three-phase process. First a Delphi study (N = 40) identified key concepts, which informed questionnaire development. Second, an exploratory factor analysis in a large, voluntary sample of Flemish secondary school students (N = 44,870) examined the underlying structure. Third, a confirmatory factor analysis in an independent student sample (N = 56,624) provided initial evidence of validity and reliability for a four-factor model with satisfactory fit and internal consistency across gender and grade. The final structure comprised class climate and engagement, peer relationships, academic self-concept, and authenticity and support, offering a practically applicable framework for monitoring students’ school well-being. Grounded in internationally established models of school well-being, Appwel is context-specific to the adolescent school setting; however, its theoretically informed format allows for careful adaptation and future examination of its applicability across different educational, cultural, and longitudinal contexts. Full article

Healthcare 5.0 and the Internet of Medical Things (IoMT) is emerging as a scalable model for the delivery of customised healthcare and chronic disease management, through Remote Patient Monitoring (RPM) in patient smart home environments. Large-scale RPM initiatives are being rolled out by healthcare providers (HCPs); however, the constrained nature of IoMT devices and proximity to poorly administered smart home technologies create a cyber risk for highly personalised patient data. The recent Network and Information Systems (NIS 2) directive requires HCPs to improve their cyber risk management approaches, mandating heavy penalties for non-compliance. Current research into cyber risk management in smart home-based RPM does not address scalability. This research examines scalability through the lens of the Non-adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework and develops a novel Scalability Index (SI), informed by a PRISMA guided systematic literature review. Our search strategy identified 57 studies across major databases including ACM, IEEE, MDPI, Elsevier, and Springer, authored between January 2016 and March 2025 (final search 21 March 2025), which focussed on cyber security risk management in the RPM context. Studies focussing solely on healthcare institutional settings were excluded. To mitigate bias, a sample of the papers (30/57) were assessed by two other raters; the resulting Cohen’s Kappa inter-rater agreement statistic (0.8) indicating strong agreement on study selection. The results, presented in graphical and tabular format, provide evidence that most cyber risk approaches do not consider scalability from the HCP perspective. Applying the SI to the 57 studies in our review resulted in a low to medium scalability potential of most cyber risk management proposals, indicating that they would not support the requirements of NIS 2 in the RPM context. A limitation of our work is that it was not tested in a live large-scale setting. However, future research could validate the proposed SI, providing guidance for researchers and practitioners in enhancing cyber risk management of large-scale RPM initiatives. Full article

We present FingerType, a one-handed text input method based on thumb-to-finger gestures. FingerType detects tap events from 3D hand data using a Temporal Convolutional Network (TCN) and decodes the tap sequence into words with an n-gram language model. To inform the design, we examined thumb-to-finger interactions and collected comfort ratings of finger regions. We used these results to design an improved T9-style key layout. Our system runs at 72 frames per second and reaches 94.97% accuracy for tap detection. We conducted a six-block user study with 24 participants and compared FingerType with controller input and touch input. Entry speed increased from 5.88 WPM in the first practice block to 10.63 WPM in the final block. FingerType also supported more eyes-free typing: attention on the display panel within ±15° of head-gaze was 84.41%, higher than touch input (69.47%). Finally, we report error patterns and WPM learning curves, and a model-based analysis suggests improving gesture recognition accuracy could further increase speed and narrow the gap to traditional VR input methods. Full article

Introduction. Although the aortic valve and mitral valve differ significantly in structure, function, and location, they both play a significant role in left ventricular (LV) function. The aim of the current study was to analyze the relationship between the mitral valve annulus (MVA) and the aortic valve annulus (AVA), as measured by three-dimensional speckle-tracking echocardiography (3DSTE) in the same healthy individuals with average or smaller/larger annular diameters (Ds), areas (As), and perimeters (Ps) in end-diastole (D) and end-systole (S). Methods. This study comprised 134 healthy adult participants with a mean age of 31.0 (16.0) years (73 males). A complete medical investigation included physical examination, laboratory tests, standard 12-lead electrocardiography, and two-dimensional Doppler echocardiography supplemented with 3DSTE. Results. Almost all end-diastolic and end-systolic MVA dimensions increased significantly with enlarging MVA. Similarly, as MVA-D-D and MVA-P-D increased, nearly all end-diastolic and end-systolic AVA dimensions exhibited a positive trend. Lower-than-average MVA-A-D was associated with a trend toward higher AVA dimensions (excluding AVA-P-D) compared to the mean MVA-A-D; conversely, higher-than-average MVA-A-D was also associated with increased AVA dimensions. AVA perimeter values were notably higher than those recorded in the lower-than-average MVA-A-D subgroup. In subjects with lower-than-average end-diastolic MVA dimensions, a non-significantly higher proportion of larger end-systolic AVA was observed relative to end-diastolic AVA. While AVA dimensions remained unchanged despite increasing MVA-D-S, a positive trend in AVA dimensions—reaching statistical significance for certain parameters—was observed alongside increasing MVA-A-S and MVA-P-S. In subjects with lower-than-average end-systolic MVA dimensions, there was a non-significantly higher prevalence of larger end-systolic AVA compared to end-diastolic AVA. Furthermore, nearly all end-diastolic and end-systolic AVA dimensions increased significantly with increasing AVA. Increases in AVA-D-D, AVA-A-D, and AVA-P-D were generally accompanied by a trend toward higher end-diastolic and end-systolic MVA dimensions; however, MVA-D-S peaked in the presence of lower-than-average end-diastolic AVA dimensions. In subjects with lower-than-average end-diastolic AVA, a non-significantly higher proportion of larger end-systolic AVA was noted compared to end-diastolic AVA. Notably higher MVA parameters were observed in the presence of mean AVA-D-S and AVA-A-S compared to their lower-than-average counterparts. Finally, end-diastolic MVA parameters showed a positive trend with increasing AVA-P-S, and subjects with higher-than-average end-systolic AVA dimensions demonstrated a significantly higher proportion of larger end-systolic AVA compared to end-diastolic AVA. Conclusions. There is a strong and complex association between the dimensions of the MVA and AVA, as assessed by 3DSTE, when measured simultaneously in the same healthy adults. Full article

Neural oscillations are fundamental to the integration of sensory, affective, and cognitive processes that contribute to pain perception. Transcranial alternating current stimulation (tACS) provides a valuable tool for investigating and modulating these oscillatory dynamics. In this review, we examine the effects of tACS on pain perception and pain-related oscillations in both healthy participants and individuals with chronic pain, highlighting methodological variability and mechanistic uncertainties that may contribute to mixed findings. We identified 14 studies, including 9 studies of experimental pain in healthy individuals and 5 of clinical pain disorders, comparing tACS to sham. Somatosensory alpha was the most frequently targeted oscillatory feature. Results varied considerably. Several studies reported reductions in pain, increases in alpha power, or changes in sensorimotor and prefrontal connectivity, but others showed no meaningful neural or behavioral effects. Out of the 14 studies, 6 demonstrated analgesic benefits and 2 showed improvements only under specific conditions or within subgroups, for a total of 8/14 studies with positive findings. Possible sources of heterogeneity include variation in stimulation duration, electrode montage, frequency alignment with individual rhythms, contextual state, and anatomical and neurophysiological differences across individuals. Pre-registered studies with sufficient power are needed to replicate effects within the most promising intervention protocols to establish a foundation in the field. We also recommend inclusion of brain imaging or electrophysiological recordings to verify whether stimulation effectively modulates the targeted neural oscillations. Finally, recent methodological advances, including phase-specific tACS, amplitude-modulated tACS, and individualized electric-field modeling, offer new opportunities to enhance mechanistic precision and clinical applicability. We argue that by integrating these approaches, future research can move beyond fixed, one-size-fits-all protocols toward personalized, state-dependent, closed-loop tACS approaches. Exploring these frontiers will transform tACS from an exploratory tool into a reliable intervention for pain. Full article

Ethnicity recognition has become increasingly important for a wide range of applications, highlighting the need for accurate and robust predictive models. Despite advances in machine learning, ethnicity classification remains a challenging research problem due to variations in facial features, class imbalance, and generalization issues. This study provides a concise synthesis of prior work to motivate the problem and then introduces a novel experimental framework for ethnicity recognition rather than a survey review. It proposes an improved approach that leverages transfer learning to enhance classification performance. The inclusion of various imaging sensors in the proposed methodology allows for an examination of how these imaging sensors impact the performance of facial recognition systems when a variety of images are captured under a number of real-world conditions, using professional and consumer-grade devices to create a range of conditions; from this dataset, the UTKFace dataset will be used to train and validate our method; an additional balanced dataset of Test Celebrities Faces was also created, representing five different ethnic groups (Black, Asian, White, Indian, and Other); the “Other” classification was specifically excluded for final evaluations to eliminate ambiguity and enhance stability. Rigorous preprocessing of both datasets was performed for optimal extraction of features from the sensors’ acquired images; the performance of several pre-trained CNN (Convolutional Neural Network) models (VGG16, DenseNet169, VGG19, ResNet50, MobileNetV2, InceptionV3 and EfficientNetB4) was used to identify an Ideal Hyperparameter Configuration for Optimal Performance. The resulting experimental results indicate that the VGG19 model achieved an 87% validation accuracy and a Maximum test accuracy of 75% on the Primary Dataset of Celebrity Faces; subsequently, the VGG19 model demonstrated a Range of Per-Class Accuracies, in addition to an overall accuracy of 87% across all five ethnic groups (51–90%+). This work demonstrates that leveraging transfer learning on imaging-sensor-captured images enables robust ethnicity classification with high accuracy and improved training efficiency relative to full model retraining. Furthermore, systematic hyperparameter optimization enhances model generalization and mitigates overfitting. Comparative experiments with recent state-of-the-art methods (2023–2025) further confirm that our optimized VGG19 model achieves competitive performance, reinforcing the effectiveness of the proposed reproducible and fairness-aware evaluation framework. Full article

This study draws on socio-technical transition theory to examine how multi-actor dynamics among producers, consumers, and the media within an experimental niche—Korea’s regulatory sandbox—shape policy responsiveness and the regulatory speed of governmental responses to emerging technologies, thereby influencing socio-technical transitions. We construct a longitudinal dataset of 2136 sandbox approvals between 2019 and 2025 and 1374 cases in which related legal or administrative adjustments have been completed. Changes in actor couplings before and after sandbox approval are first assessed using Pearson correlation analysis, while temporal lead–lag relationships are identified via vector autoregression (VAR) and Granger causality tests. Building on these dynamic analyses, the study subsequently investigates the determinants of regulatory response speed using ordered logistic regression, incorporating government policy orientation (progressive vs. conservative) as a moderating variable. The results show, first, that the strong producer–consumer coupling observed prior to sandbox approval weakens afterwards, whereas the consumer–media linkage becomes substantially stronger. Second, the time-series analysis of technologies within the regulatory sandbox reveals a typical technology-push pattern and a self-reinforcing feedback loop. Specifically, producer activity initiates the signal sequence, preceding consumer reactions; subsequently, media coverage significantly drives consumer engagement, and the resulting increase in consumer attention, in turn, stimulates further media coverage. Third, in the ordered logit model, media activity accelerates legal and regulatory reform, whereas consumer activity acts as a delaying factor, with producer activity showing no significant direct effect. Finally, government policy orientation systematically moderates the magnitude and direction of these effects. Overall, the study proposes an actor-centered mechanism in which learning generated in the sandbox is externalized through consumer–media channels and translated into regulatory pacing. Based on these findings, we derive practical implications for firms and regulators regarding proactive media engagement, transparent use of evidence, institutionalized channels for consumer input, and robust feedback standards that support sustainable commercialization of emerging technologies. Full article