Search Results (1,808)

Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems. Full article

Humanoid robots have attracted considerable attention for their anthropomorphic structure, extended workspace, and versatile capabilities. This paper presents a novel humanoid upper-body robotic system comprising a pair of 8-degree-of-freedom (DOF) arms, a 3-DOF head, and a 3-DOF torso—yielding a 22-DOF architecture inspired by human biomechanics and implemented via standardized hollow joint modules. To overcome the critical reliance of zeroing neural network (ZNN)-based trajectory tracking on the Jacobian matrix derivative, we propose an integration-enhanced matrix derivative observer (IEMDO) that incorporates nonlinear feedback and integral correction. The observer is theoretically proven to ensure asymptotic convergence and enables accurate, real-time estimation of matrix derivatives, addressing a fundamental limitation in conventional ZNN solvers. Workspace analysis reveals that the proposed design achieves an 87.7% larger total workspace and a remarkable 3.683-fold expansion in common workspace compared to conventional dual-arm baselines. Furthermore, the observer demonstrates high estimation accuracy for high-dimensional matrices and strong robustness to noise. When integrated into the ZNN controller, the IEMDO achieves high-precision trajectory tracking in both simulation and real-world experiments. The proposed framework provides a practical and theoretically grounded approach for redundant humanoid arm control. Full article

Objectives: This study explored perceptions, experiences, and outcomes associated with the choice of insulin therapies among pediatric patients with type 1 diabetes mellitus (T1D). Methods: This study included 20 adolescents (8 male and 12 female) with T1D, with a mean age of 15.05 ± 0.91 years, a mean diabetes duration of 5.19 ± 1.2 years, and a mean most recent HbA1c of 7.03 ± 0.16%. Ten of the participants were using an insulin pump (n = 10) and another 10 had either refused (n = 7) or discontinued (n = 3) insulin pump therapy. A qualitative inductive method was employed, using in-depth individual interviews. The interview material was transcribed verbatim and grounded theory was used to analyze the verbal material. Results: Four main thematic categories were identified from the narrations that captured both common and divergent perceptions of insulin pump users versus non-users: (1) adjusting to the lifelong diagnosis, (2) exposing diabetes versus hiding it, (3) becoming autonomous and integrating insulin pump therapy into daily life, and (4) worrying over the pump. The third theme, capturing autonomy and integration, surfaced as the core thematic category of this study. Conclusions: This grounded theory study revealed that, by using insulin pump therapy, adolescent T1D patients can enhance their autonomy and facilitate the integration of insulin treatment into their life. This study identified processes that inform diabetes education and contribute to ameliorating gaps in the uptake and maintenance of pump therapy in pediatric care. Full article

Yellow rust (Puccinia striiformis) is a common wheat disease that significantly reduces yields, particularly in seasons with cooler temperatures and frequent rainfall. Early detection is essential for effective control, especially in key wheat-producing regions such as Southern Dobrudja, Bulgaria. This study presents a UAV-based approach for detecting yellow rust using only RGB imagery and deep learning for pixel-based classification. The methodology involves data acquisition, preprocessing through histogram equalization, model training, and evaluation. Among the tested models, a UnetClassifier with ResNet34 backbone achieved the highest accuracy and reliability, enabling clear differentiation between healthy and infected wheat zones. Field experiments confirmed the approach’s potential for identifying infection patterns suitable for precision fungicide application. The model also showed signs of detecting early-stage infections, although further validation is needed due to limited ground-truth data. The proposed solution offers a low-cost, accessible tool for small and medium-sized farms, reducing pesticide use while improving disease monitoring. Future work will aim to refine detection accuracy in low-infection areas and extend the model’s application to other cereal diseases. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

The need for systems that forecast and respond proactively to meteorological disasters is growing amid climate variability. Although the early warning system in South Korea includes countermeasure information, it remains limited in terms of data recency, granularity, and regional adaptability. Additionally, its closed architecture hinders interoperability with external systems. This study aims to redesign the countermeasure function as an independent cloud-based platform grounded in the common standard terminology framework in South Korea. A multi-dimensional data model was developed using attributes such as crop type, cultivation characteristics, growth stage, disaster type, and risk level. The platform incorporates user-specific customization features and history tracking capabilities, and it is structured using a microservices architecture to ensure modularity and scalability. The proposed system enables real-time management and dissemination of localized countermeasure suggestions tailored to various user types, including central and local governments and farmers. This study offers a practical model for enhancing the precision and applicability of agrometeorological response information. It is expected to serve as a scalable reference platform for future integration with external agricultural information systems. Full article

Recent findings highlight that Reelin, a glycoprotein involved in neural development, synaptic plasticity, and neuroinflammation, plays some specific roles in neurodegenerative disorders associated with aging, such as age-related macular degeneration (AMD) and Alzheimer’s disease (AD). Reelin modulates synaptic function and guarantees homeostasis in neuronal-associated organs/tissues (brain and retina). The expression of Reelin is dysregulated in these neurological disorders, showing common pathways depending on chronic neurogenic inflammation and/or dysregulation of the extracellular matrix in which Reelin plays outstanding roles. Recently, the relationship between AMD and AD has gained increasing attention as they share many common risk factors (aging, genetic/epigenetic background, smoking, and malnutrition) and histopathological lesions, supporting certain pathophysiological crosstalk between these two diseases, especially regarding neuroinflammation, oxidative stress, and vascular complications. Outside the nervous system, Reelin is largely produced at the gastrointestinal epithelial level, in close association with innervated regions. The expression of Reelin receptors inside the gut suggests interesting aspects in the field of the gut–brain–eye axis, as dysregulation of the intestinal microbiota has been frequently described in neurodegenerative and behavioral disorders (AD, autism, and anxiety and/or depression), most probably linked to inflammatory, neurogenic mediators, including Reelin. Herein we examined previous and recent findings on Reelin and neurodegenerative disorders, offering findings on Reelin’s potential relation with the gut–brain and gut–brain–eye axes and providing novel attractive hypotheses on the gut–brain–eye link through neuromodulator and microbiota interplay. Neurodegenerative disorders will represent the ground for a future starting point for linking the common neurodegenerative biomarkers (β-amyloid and tau) and the new proteins probably engaged in counteracting neurodegeneration and synaptic loss. Full article

In the 2021 summit, ASEAN leaders acknowledged the ocean as an essential driver of economic recovery post pandemic, leading to the ASEAN Declaration on the Blue Economy for the responsible management of marine resources. As an ASEAN nation with a long history in the fishing sector, Indonesia then actively spread this concept across the region. The hegemony theory of Gramsci, which considers the interaction of a nation’s material resources, ideational influence, and institutional strategy, is further used to assess Indonesia’s leadership dynamics in the ASEAN to obtain consensus-based power. In this study, Joko Widodo’s speeches from 2023 are taken out and coded to determine the narrative that Indonesia constantly reinforces. With thematic analysis, speech data is processed to generate keywords such as unity, cooperation, and shared responsibilities, which Indonesia often uses to advance its regional agenda. By aligning member states’ interests with regional goals, Indonesian governance creates common ground for a blue economy and emphasizes how the sea is an integral source of opportunity for the region’s position as the Epicentrum Of Growth. Instead of pushing countries to agree with directives, Indonesia effectively advocates for regional agreements and ASEAN-led structures through the blue economy framework, with the ABEF emerging at its 2023 ASEAN chairmanship deliberations. Full article

The integration of unmanned aerial vehicles (UAVs) into next-generation wireless networks greatly enhances the flexibility and efficiency of communication and distributed computation for ground mobile devices. Federated learning (FL) provides a privacy-preserving paradigm for device collaboration but remains highly vulnerable to poisoning attacks and is further challenged by the resource constraints and heterogeneous data common to UAV-assisted systems. Existing robust aggregation and anomaly detection methods often degrade in efficiency and reliability under these realistic adversarial and non-IID settings. To bridge these gaps, we propose FedULite, a lightweight and robust federated learning framework specifically designed for UAV-assisted environments. FedULite features unsupervised local representation learning optimized for unlabeled, non-IID data. Moreover, FedULite leverages a robust, adaptive server-side aggregation strategy that uses cosine similarity-based update filtering and dimension-wise adaptive learning rates to neutralize sophisticated data and model poisoning attacks. Extensive experiments across diverse datasets and adversarial scenarios demonstrate that FedULite reduces the attack success rate (ASR) from over 90% in undefended scenarios to below 5%, while maintaining the main task accuracy loss within 2%. Moreover, it introduces negligible computational overhead compared to standard FedAvg, with approximately 7% additional training time. Full article

Background: Tremor is a common but diagnostically challenging movement disorder due to its clinical heterogeneity and overlapping aetiologies. The 2018 consensus introduced a two-axis classification system that redefined tremor syndromes by distinguishing between clinical phenomenology and underlying causes, and introduced new diagnostic categories, such as essential tremor plus. Methods: This review synthesises recent advances in the epidemiology, classification, pathophysiology, and treatment of tremor syndromes, aiming to provide an integrated and clinically relevant framework that aligns with emerging diagnostic and therapeutic paradigms. Results: We discuss how electrophysiology, neuroimaging, wearable sensors, and artificial intelligence are reshaping diagnostic precision. Syndromes such as essential tremor, parkinsonian tremor, dystonic tremor, task-specific tremor, orthostatic tremor, and functional tremor are examined through syndromic, aetiological, and mechanistic lenses. The limitations of current rating scales and the promise of emerging biomarkers are critically assessed. Conclusions: As therapeutic approaches evolve toward neuromodulation and precision medicine, the need for pathophysiologically grounded diagnostic criteria becomes more urgent. Integrating network-based frameworks, digital diagnostics, and individualised treatment holds promise for advancing tremor care. Full article

In the Chan Buddhism koan (gong’an 公案) tradition, the act of “slaying the snake” functions as a signature gesture imbued with complex, historically layered cultural meanings. Rather than merely examining its motivations, this paper emphasizes tracing the semantic transformations that this motif has undergone across different historical contexts. It argues that “snake-slaying” operated variously as an imperial narrative strategy reinforcing ruling class ideology; as a form of popular resistance by commoners against flood-related disasters; as a dietary practice among aristocrats and literati seeking danyao (elixirs) 丹藥 for reclusion and transcendence; and ultimately, within the Chan tradition, as a method of spiritual cultivation whereby masters sever desires rooted in attachment to both selfhood and the Dharma. More specifically, first, as an imperial narrative logic, snake-slaying embodied exemplary power: both Liu Bang 劉邦 and Guizong 歸宗 enacted this discursive strategy, with Guizong’s legitimacy in slaying the snake deriving from the precedent set by Liu Bang. Second, as a folk strategy of demystification, snake-slaying acquired a moral aura—since the snake was perceived as malevolent force, their slaying appeared righteous and heroic. Finally, as a mode of self-cultivation among the aristocracy, snake-slaying laid the groundwork for its later internalization. In Daoism, slaying the snake was a means of cultivating the body; in Chan Buddhism, the act is elevated to a higher plane—becoming a way of cultivating the mind. This transformation unfolded naturally, as if predestined. In all cases, the internalization of the snake-slaying motif was not an overnight development: the cultural genes that preceded its appearance in the Chan tradition provided the fertile ground for its karmic maturation and discursive proliferation. Full article

The accurate monitoring of waterbird abundance and their habitat preferences is essential for effective ecological management and conservation planning in aquatic ecosystems. This study explores the efficacy of unmanned aerial vehicle (UAV)-based high-resolution orthomosaics for waterbird monitoring and mapping along the Lieve Canal, Belgium. We systematically classified habitats into residential, industrial, riparian tree, and herbaceous vegetation zones, examining their influence on the spatial distribution of three focal waterbird species: Eurasian coot (Fulica atra), common moorhen (Gallinula chloropus), and wild duck (Anas platyrhynchos). Herbaceous vegetation zones consistently supported the highest waterbird densities, attributed to abundant nesting substrates and minimal human disturbance. UAV-based waterbird counts correlated strongly with ground-based surveys (R² = 0.668), though species-specific detectability varied significantly due to morphological visibility and ecological behaviors. Detection accuracy was highest for coots, intermediate for ducks, and lowest for moorhens, highlighting the crucial role of image resolution ground sampling distance (GSD) in aerial monitoring. Operational challenges, including image occlusion and habitat complexity, underline the need for tailored survey protocols and advanced sensing techniques. Our findings demonstrate that UAV imagery provides a reliable and scalable method for monitoring waterbird habitats, offering critical insights for biodiversity conservation and sustainable management practices in aquatic landscapes. Full article

Ports in the Baltic Sea region play an integral role in advancing sustainable maritime practices in the area, due to their geographic interconnectedness, economic importance, and sensitivity to environmental challenges. While numerous port sustainability assessment methods exist, most of which are grounded in the Triple Bottom Line (TBL) metric, many tend to emphasise whether specific targets have been met, rather than evaluating port sustainability on a scalar basis. This study explores the sustainability strategies of seven selected ports in five Baltic Sea countries using an innovative qualitative evaluation framework developed by the Swedish Maritime Competence Centre (SMCC). The SMCC model integrates the three core pillars of sustainability-environmental, social, and economic dimensions, while incorporating energy efficiency and digitalisation as critical enablers of modern port operations. The findings reveal significant variation in sustainability performance among the selected ports, shaped by regional contexts, operational profiles, and prior engagement with sustainability initiatives. Also, the results bring into light the most common sustainable practices used in the ports, e.g., LED lightning, onshore power supply, and port information systems. Full article

This paper introduces a unified and systematic design methodology for robotic systems that is generalizable across a wide range of applications. It integrates rigorous mathematical formalisms such as kinematics, dynamics, control theory, and optimization with advanced simulation tools, ensuring that each design decision is grounded in provable theory. The approach defines clear phases, including mathematical modeling, virtual prototyping, parameter optimization, and theoretical validation. Each phase builds on the previous one to reduce unforeseen integration issues. Spanning from conceptualization to deployment, it offers a blueprint for developing mathematically valid and robust robotic solutions while streamlining the transition from design intent to functional prototype. By standardizing the design workflow, this framework reduces development time and cost, improves reproducibility across projects, and enhances collaboration among multidisciplinary teams. Such a generalized approach is essential in today’s fast-evolving robotics landscape where rapid innovation and cross-domain applicability demand flexible yet reliable methodologies. Moreover, it provides a common language and set of benchmarks that both novice and experienced engineers can use to evaluate performance, facilitate knowledge transfer, and future-proof systems against emerging application requirements. Full article

In this study, We explore for Minkowski 3-space ${\mathbb{E}}_1^3$ harmonic surfaces’ geometric features by employing a common tangent vector field along a curve situated on the surface. Our analysis is grounded in the rotation minimizing (RM) Darboux frame, which offers a robust alternative to the classical Frenet frame particularly valuable in the Lorentzian setting, where singularities frequently arise. The RM Darboux frame, tailored to curves lying on surfaces, enables the expression of fundamental invariants such as geodesic curvature, normal curvature, and geodesic torsion. We derive specific conditions that characterize harmonic surfaces based on these invariants. We also clarify the connection between the components of the RM Darboux frame and thesurface’s mean curvature vector. This formulation provides fresh perspectives on the classification and intrinsic structure of harmonic surfaces within Minkowski geometry. To support our findings, we present several illustrative examples that demonstrate the applicability and strength of the RM Darboux approach in Lorentzian differential geometry. Full article