Search Results (1,009)

This paper systematically reviews the research progress of underwater soft grasping devices in the field of bionic structure, function integration, and tactile sensing technology by drawing on the structural characteristics of marine organisms such as octopuses, jellyfish, and sea anemones (such as suction cups, umbrella-like muscles, and stinging cells). This paper analyzes the inspiration for the design, the application of innovative materials, and the integration of sensing and driving from marine organisms, including a review of soft robotics technologies, such as shape memory alloys (SMA), ionic polymer metal composite materials (IPMCs), magnetic nanocomposite cilia, etc. The research results emphasize that bionic soft robots have the potential for transformation in completely changing underwater operations by providing enhanced flexibility, efficiency, and environmental adaptability. This work provides a bionic design paradigm and perception-driven integration method for underwater soft operation systems, thereby promoting equipment innovation in the fields of deep-sea exploration and ecological protection. Full article

Event-based cameras, as a revolutionary class of dynamic vision sensors, offer transformative advantages for capturing transient mechanical phenomena through their asynchronous, per-pixel brightness change detection mechanism. These neuromorphic sensors excel in challenging industrial environments with their microsecond-level temporal resolution, ultra-low power requirements, and exceptional dynamic range that significantly outperform conventional imaging systems. In this way, the event-based camera provides a promising tool for machine vibration sensing and fault diagnosis. However, the dynamic vision data from the event-based cameras have a complex structure, which cannot be directly processed by the mainstream methods. This paper proposes a dynamic vision-based non-contact machine fault diagnosis method. The Eagle Vision Transformer (EViT) architecture is proposed, which incorporates biologically plausible computational mechanisms through its innovative Bi-Fovea Self-Attention and Bi-Fovea Feedforward Network designs. The proposed method introduces an original computational framework that effectively processes asynchronous event streams while preserving their inherent temporal precision and dynamic response characteristics. The proposed methodology demonstrates exceptional fault diagnosis performance across diverse operational scenarios through its unique combination of multi-scale spatiotemporal feature analysis, adaptive learning capabilities, and transparent decision pathways. The effectiveness of the proposed method is extensively validated by the practical condition monitoring data of rotating machines. By successfully bridging cutting-edge bio-inspired vision processing with practical industrial monitoring requirements, this work creates a new paradigm for dynamic vision-based non-contact machinery fault diagnosis that addresses critical limitations of conventional approaches. The proposed method provides new insights for predictive maintenance applications in smart manufacturing environments. Full article

Robotic manipulators in warehousing and logistics often face complex tasks that involve multiple steps, frequent task switching, and long-term dependencies. Inspired by the hierarchical structure of human motor control, this paper proposes a Hierarchical Reinforcement Learning (HRL) framework utilizing a multi-task goal-conditioned Decision Transformer (MTGC-DT). The high-level policy treats the Markov decision process as a sequence modeling task, allowing the agent to manage temporal dependencies. The low-level policy is made up of parameterized action primitives that handle physical execution. This design improves long-term reasoning and generalization. This method is evaluated on two common logistics manipulation tasks: sequential stacking and spatial sorting with sparse reward and low-quality dataset. The main contributions include introducing a HRL framework that integrates Decision Transformer (DT) with task and goal embeddings, along with a path-efficiency loss (PEL) correction and designing a parameterized, learnable primitive skill library for low-level control to enhance generalization and reusability. Experimental results demonstrate that the proposed Decision Transformer-based Hierarchical Reinforcement Learning (DT-HRL) achieves over a 10% higher success rate and over 8% average reward compared with the baseline, and a normalized score increase of over 2% in the ablation experiments. Full article

Agriculture is fundamental to food security and environmental sustainability. Advancing its holistic ecological transformation can stimulate socioeconomic progress while fostering human–nature harmony. Utilizing provincial data from mainland China (2013–2022), this research establishes a multidimensional evaluation framework across four pillars: agricultural ecology, low-carbon practices, modernization, and productivity enhancement. Through comprehensive assessment, we quantify China’s low-carbon green agriculture (LGA) development trajectory and conduct comparative regional analysis across eastern, central, and western zones. As for methods, this study employs multiple econometric approaches: LGA was quantified using the TOPSIS entropy weight method at the first step. Moreover, multidimensional spatial–temporal patterns were characterized through ArcGIS spatial analysis, Dagum Gini coefficient decomposition, Kernel density estimation, and Markov chain techniques, revealing regional disparities, evolutionary trajectories, and state transition dynamics. Last but not least, Tobit regression modeling identified driving mechanisms, informing improvement strategies derived from empirical evidence. The key findings reveal the following: 1. From 2013 to 2022, LGA in China fluctuated significantly. However, the current growth rate is basically maintained between 0% and 10%. Meanwhile, LGA in the vast majority of provinces exceeds 0.3705, indicating that LGA in China is currently in a stable growth period. 2. After 2016, the growth momentum in the central and western regions continued. The growth rate peaked in 2020, with some provinces having a growth rate exceeding 20%. Then the growth rate slowed down, and the intra-regional differences in all regions remained stable at around 0.11. 3. Inter-regional differences are the main factor causing the differences in national LGA, with contribution rates ranging from 67.14% to 74.86%. 4. LGA has the characteristic of polarization. Some regions have developed rapidly, while others have lagged behind. At the end of our ten-year study period, LGA in Yunnan, Guizhou and Shanxi was still below 0.2430, remaining in the low-level range. 5. In the long term, the possibility of improvement in LGA in various regions of China is relatively high, but there is a possibility of maintaining the status quo or “deteriorating”. Even provinces with a high level of LGA may be downgraded, with possibilities ranging from 1.69% to 4.55%. 6. The analysis of driving factors indicates that the level of economic development has a significant positive impact on the level of urban development, while the influences of urbanization, agricultural scale operation, technological input, and industrialization level on the level of urban development show significant regional heterogeneity. In summary, during the period from 2013 to 2022, although China’s LGA showed polarization and experienced ups and downs, it generally entered a period of stable growth. Among them, the inter-regional differences were the main cause of the unbalanced development across the country, but there was also a risk of stagnation and decline. Economic development was the general driving force, while other driving factors showed significant regional heterogeneity. Finally, suggestions such as differentiated development strategies, regional cooperation and resource sharing, and coordinated policy allocation were put forward for the development of LGA. This research is conducive to providing references for future LGA, offering policy inspirations for LGA in other countries and regions, and also providing new empirical results for the academic community. Full article

The Black Church has historically functioned as both a spiritual sanctuary and a catalyst for sociopolitical transformation within African American communities. This article investigates how ecclesiological identity has evolved in diaspora contexts, particularly through the lens of the African American experience. Tracing its roots from African spiritual traditions and the era of slavery, through emancipation, the Great Migration, and the Civil Rights Movement, to the digital age and megachurch phenomenon, the Black Church has continually adapted to shifting cultural, theological, and social landscapes. Using a multidisciplinary approach that includes historical theology, sociology, and cultural analysis, this study explores how these adaptations reveal an ecclesiology grounded in liberation, justice, and resilience. Theologically, this paper contends that the Black Church’s ecclesial model offers a prophetic and globally relevant witness that challenges systemic injustice while inspiring communal hope. In examining both past and present adaptations, the article contributes to broader conversations around diasporic faith identity, theological innovation, and the global role of the Black Church. Full article

We present a novel encoder-only Transformer model for symbolic music harmony generation, based on a fixed time-grid representation of melody and harmony. Inspired by denoising diffusion processes, our model progressively unmasks harmony tokens over a sequence of discrete stages, learning to reconstruct the full harmonic structure from partial context. Unlike autoregressive models, this formulation enables flexible, non-sequential generation and supports explicit control over harmony placement. The model is stage-aware, receiving timestep embeddings analogous to diffusion timesteps, and is conditioned on both a binary piano roll and a pitch class roll to capture melodic context. We explore two unmasking schedules—random token revealing and midpoint doubling—both requiring a fixed and significantly reduced number of model calls at inference time. While our approach achieves competitive performance with strong autoregressive baselines (GPT-2 and BART) across several harmonic metrics, its key advantages lie in controllability, structured decoding with fixed inference steps, and alignment with musical structure. Ablation studies further highlight the role of stage awareness and pitch class conditioning. Our results position this method as a viable and interpretable alternative for symbolic harmony generation and a foundation for future work on structured, controllable musical modeling. Full article

Despite the remarkable developments in advanced materials, silicon and gallium arsenide remain among the leading semiconductors of our time. Nanomechanical studies of these semiconductor crystals, including nanoindentation-induced structural phase transformations and dislocation generation, remain important for science and technology. Of particular interest are studies on the onset of plasticity. What phenomenon initiates plastic deformation in Si and GaAs during nanoindentation? Through complex experiments and computer simulations, significant progress has been made in answering this question over the past twenty years. Indeed, equipping nanoindentation systems with the ability to record Raman spectra and exploring new interatomic interaction models for classical molecular dynamics have opened up new avenues for studying the non-trivial interplay between structural phase transformations and dislocation activity in semiconductor crystals. The diversity of high-pressure phases, especially silicon, and the largely unexplored sequences of transformations between them continue to inspire new scientific challenges. This article reviews selected works introducing the reader to the fascinating and still open topic of nanoindentation-induced incipient plasticity in silicon and gallium arsenide. Full article

Sierra Leone possesses distinct educational, economic, and social characteristics. Public universities in the country, funded by the government, are mandated to promote sustainable development, ethical conduct, and social welfare, aligning with national development strategies such as the Midterm National Development Plan and the Education Sector Plan, which emphasize leadership, diversity, and ethical standards to advance sustainable development practices. This study applies Transformational Leadership Theory to investigate the influence of transformational leadership on corporate social responsibility, exploring the mediating role of innovative work behavior and the moderating effect of organizational culture on this relationship. Using a stratified sampling technique, data were collected from 367 employees across six public universities in Sierra Leone and analyzed with SMART PLS software 4.1.1.2. The findings revealed that transformational leadership positively and significantly impacts corporate social responsibility and innovative work behavior, with innovative work behavior partially mediating the relationship between transformational leadership and corporate social responsibility, while organizational culture positively and significantly moderates this relationship. Based on these findings, the study recommends that public universities in Sierra Leone integrate transformational leadership principles into their institutional frameworks to improve organizational outcomes and leadership effectiveness. This can be achieved through leadership development programs emphasizing transformational attributes such as inspirational motivation, individualized consideration, and vision-sharing, alongside mentorship programs for leaders at all levels to strengthen leadership skills and foster an organizational culture aligned with institutional goals. Full article

Anxiety and depression affect millions worldwide, yet stigma and long wait times often delay access to care. Mobile mental health apps can decrease these barriers by offering on-demand screening and support. Nevertheless, many machine and deep learning methods used in such tools perform poorly under severe class imbalance, yielding biased, poorly calibrated predictions. To address this challenge, this study proposes MCoG-LDPSNet, a brain-inspired model that combines dual, orthogonal encoding pathways with a novel Loss-Driven Parametric Swish (LDPS) activation. LDPS implements a neurobiologically motivated adaptive-gain mechanism via a learnable β parameter driven by calibration and confidence-aware loss signals that amplifies minority-class patterns while preserving overall reliability, enabling robust predictions under severe data imbalance. On a benchmark mental health corpus, MCoG-LDPSNet achieved AUROC = 0.9920 and G-mean = 0.9451, outperforming traditional baselines like GLMs, XGBoost, state-of-the-art deep models (CNN-BiLSTM-ATTN), and transformer-based approaches. After transfer learning to social media text, the MCoG-LDPSNet maintained a near-perfect AUROC of 0.9937. Integrated into the EmotiZen App with enhanced app features, MCoG-LDPSNet was associated with substantial symptom reductions (anxiety 28.2%; depression 42.1%). These findings indicate that MCoG-LDPSNet is an accurate, imbalance-aware solution suitable for scalable mobile screening of individuals for anxiety and depression. Full article

Glocalization plays a vital role in promoting regionally embedded sustainable development by enabling territories to adapt global economic impulses to local capacities, values, and institutional frameworks. This paper develops a framework for the quantitative assessment of economic glocalization at the regional level, focusing on the European Union. Drawing on the conceptual metaphor of “refraction”, glocalization is interpreted as a transformation of global economic impulses as they pass through and interact with localized socio-economic structures. The authors construct a Glocalization Index System comprising three sub-indices: (1) Index of Generation of Globalization Impulses, (2) Index of Resistance to Globalization Impulses, and (3) Index of Transformation of Globalization Impulses. Each sub-index integrates normalized indicators related to regional creativity—conceptualized through the four “I”s: Institutions, Intelligence, Inspiration, and Infrastructure—as well as trade and investment dynamics. The empirical analysis reveals substantial interregional variation in glocalization capacities, with regions of Germany, the Netherlands, Sweden, and Finland ranking among the most prominent generators and transformers of globalization impulses. Strong correlations are observed between the Resistance and Transformation indices, supporting the hypothesis that medium resistance levels contribute most effectively to transformation processes. By integrating both global (exogenous) and local (endogenous) dimensions, the proposed framework not only addresses a gap in economic literature but also offers a tool for guiding policies aimed at sustainable, adaptive, and innovation-driven regional development. Full article

Background: Studies have shown that humans can rapidly learn the shape of new objects or adjust their behavior when encountering novel situations. Research on visual cognition in the brain further indicates that the ventral visual pathway plays a critical role in core object recognition. While existing studies often focus on microscopic simulations of individual neural structures, few adopt a holistic, system-level perspective, making it difficult to achieve robust few-shot learning capabilities. Method: Inspired by the mechanisms and processes of the ventral visual stream, this paper proposes a computational model with a macroscopic neural architecture for few-shot learning. We reproduce the feature extraction functions of V1 and V2 using a well-trained Vision Transformer (ViT) and model the neuronal activity in V4 and IT using two neural fields. By connecting these neurons based on Hebbian learning rules, the proposed model stores the feature and category information of the input samples during support training. Results: By employing a scale adaptation strategy, the proposed model emulates visual neural mechanisms, enables efficient learning, and outperforms state-of-the-art few-shot learning algorithms in comparative experiments on real-world image datasets, demonstrating human-like learning capabilities. Conclusion: Experimental results demonstrate that our ventral-stream-inspired machine-learning model achieves effective few-shot learning on real-world datasets. Full article

Background/Objectives: Acute Lymphoblastic Leukemia (ALL) poses significant diagnostic challenges due to its ambiguous symptoms and the limitations of conventional methods like bone marrow biopsies and flow cytometry, which are invasive, costly, and time-intensive. Methods: This study introduces Neuro-Bridge-X, a novel neuro-symbolic hybrid model designed for automated, explainable ALL diagnosis using peripheral blood smear (PBS) images. Leveraging two comprehensive datasets, ALL Image (3256 images from 89 patients) and C-NMC (15,135 images from 118 patients), the model integrates deep morphological feature extraction, vision transformer-based contextual encoding, fuzzy logic-inspired reasoning, and adaptive explainability. To address class imbalance, advanced data augmentation techniques were applied, ensuring equitable representation across benign and leukemic classes. The proposed framework was evaluated through 5-fold cross-validation and fixed train-test splits, employing Nadam, SGD, and Fractional RAdam optimizers. Results: Results demonstrate exceptional performance, with SGD achieving near-perfect accuracy (1.0000 on ALL, 0.9715 on C-NMC) and robust generalization, while Fractional RAdam closely followed (0.9975 on ALL, 0.9656 on C-NMC). Nadam, however, exhibited inconsistent convergence, particularly on C-NMC (0.5002 accuracy). A Meta-XAI controller enhances interpretability by dynamically selecting optimal explanation strategies (Grad-CAM, SHAP, Integrated Gradients, LIME), ensuring clinically relevant insights into model decisions. Conclusions: Visualizations confirm that SGD and RAdam models focus on morphologically critical features, such as leukocyte nuclei, while Nadam struggles with spurious attributions. Neuro-Bridge-X offers a scalable, interpretable solution for ALL diagnosis, with potential to enhance clinical workflows and diagnostic precision in oncology. Full article

Maximizing hydrocarbon recovery from mature and complex reservoirs is constrained by heterogeneity, sand production, and harsh operational conditions. While polyacrylamide (PAM)-based systems are pivotal in addressing these challenges, a comprehensive synthesis of their transformative evolution and multifunctional capabilities remains overdue. This review critically analyzes advancements in PAM-based materials for enhanced oil recovery (EOR), conformance control, and sand management. We show that nanomaterial integration (e.g., magnetic NPs, nanoclays) significantly augments PAM’s rheological control, thermal and salinity stability, interfacial properties, and wettability alteration. Furthermore, the emergence of supramolecular chemistry has endowed PAM systems with unprecedented resilience, enabling self-healing and adaptive performance under extreme subsurface conditions. The review highlights a crucial paradigm shift towards integrated reservoir management, synergizing these advanced chemical designs with mechanical strategies and leveraging sophisticated monitoring and predictive analytics. Critically, innovations in sustainable and bio-inspired PAM materials offer environmentally responsible solutions with enhanced biodegradability. This synthesis provides a holistic understanding of the state of the art. Despite persistent challenges in scalability and predictability, continually re-engineered PAM systems are positioned as an indispensable and increasingly sustainable cornerstone for future hydrocarbon recovery in the complex energy landscape. Full article

Inspired by the interpretability of Kolmogorov–Arnold Networks (KANs), a novel Pixel-level Feature Selection (PFS) model based on KANs (PFSKANs) is proposed as a fundamentally distinct alternative from trainable Convolutional Neural Networks (CNNs) and transformers in the computer vision tasks. We modify the simplification techniques of KANs to detect key pixels with high contribution scores directly at the input image. Specifically, a trainable selection procedure is intuitively visualized and performed only once, since the obtained interpretable pixels can subsequently be identified and dimensionally standardized using the proposed mathematical approach. Experiments on the image classification tasks using the MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate that PFSKANs achieve comparable performance to CNNs in terms of accuracy, parameter efficiency, and training time. Full article

Inspired by the evolutionary optimization of biological load-bearing systems, honeycomb structures are highly valued in applications involving impact protection and lightweight load-bearing due to their outstanding mechanical properties. This study introduces an interesting honeycomb structure known as the hybrid topological cellular honeycomb structure (HTCHS), which integrates four distinctive topological cells. To effectively fabricate HTCHS samples, the research utilized a fused deposition modeling (FDM) process, employing polyethylene terephthalate glycol-modified (PETG) as the matrix material, successfully producing the HTCHS samples. A finite element simulation model for the HTCHS is created using LS-DYNA software(LS-DYNA R11.1.0 software), and its accuracy is confirmed through a comparative analysis of experimental and simulation results. The influence of the topological cell parameters (T₁ to T₄) on compressive energy absorption, specific energy absorption, and peak crushing force through parametric modeling is investigated. The mechanical properties of honeycomb structures vary depending on the cell parameters at different positions, and monotonically increasing the design parameters does not improve the energy absorption capacity of the HTCHS. To enhance the mechanical performance of the HTCHS, the initial periodic cell configurations are transformed into non-periodic designs. A discrete optimization design framework for local parameters of the HTCHS is established, integrating cell coding with the MOPSO algorithm. The feasibility of the optimization results is validated through experimental data, demonstrating that this study offers an effective technical solution for developing a novel generation of cellular honeycomb structures with customizable mechanical properties. Full article