Search Results (573)

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions. Full article

The performance of the UAVs while executing various mission profiles greatly depends on the selection of planning algorithms. Reinforcement learning (RL) algorithms can effectively be utilized for robot path planning. Due to random action selection in case of action ties, the traditional Q-learning algorithm and its other variants face the issues of slow convergence and suboptimal path planning in high-dimensional navigational environments. To solve these problems, we propose an improved deep Q-network (DQN), incorporating an efficient tie-breaking mechanism, prioritized experience replay (PER), and L2-regularization. The adopted tie-breaking mechanism improves the action selection and ultimately helps in generating an optimal trajectory for the UAV in a 3D cluttered environment. To improve the convergence speed of the traditional Q-algorithm, prioritized experience replay is used, which learns from experiences with high temporal difference (TD) error and avoids uniform sampling of stored transitions during training. This also allows the prioritization of high-reward experiences (e.g., reaching a goal), which helps the agent to rediscover these valuable states and improve learning. Moreover, L2-regularization is adopted that encourages smaller weights for more stable and smoother Q-values to reduce the erratic action selections and promote smoother UAV flight paths. Finally, the performance of the proposed method is presented and thoroughly compared against the traditional DQN, demonstrating its superior effectiveness. Full article

As digital transformation accelerates across Europe, cloud computing plays an increasingly central role in modernizing public services and private enterprises. Yet adoption rates vary markedly among EU member states, reflecting deeper structural differences in digital capacity. This study employs explainable machine learning to uncover the drivers of national cloud adoption across 27 EU countries using harmonized panel datasets spanning 2014–2021 and 2014–2024. A methodological pipeline combining Random Forests (RF), XGBoost, Support Vector Machines (SVM), and Elastic Net regression is implemented, with model tuning conducted via nested cross-validation. Among individual models, Elastic Net and SVM delivered superior predictive performance, while a stacked ensemble achieved the best overall accuracy (MAE = 0.214, R² = 0.948). The most interpretable model, a standardized RF with country fixed effects, attained MAE = 0.321, and R² = 0.864, making it well-suited for policy analysis. Variable importance analysis reveals that the density of ICT specialists is the strongest predictor of adoption, followed by broadband access and higher education. Fixed-effect modeling confirms significant national heterogeneity, with countries like Finland and Luxembourg consistently leading adoption, while Bulgaria and Romania exhibit structural barriers. Partial dependence and SHAP analyses reveal nonlinear complementarities between digital skills and infrastructure. A hierarchical clustering of countries reveals three distinct digital maturity profiles, offering tailored policy pathways. These results directly support the EU Digital Decade’s strategic targets and provide actionable insights for advancing inclusive and resilient digital transformation across the Union. Full article

Wheel loaders in mines and construction sites repeatedly load soil from a pile to load receivers. Automating this task presents a challenging planning problem since each loading’s performance depends on the pile state, which depends on previous loadings. We investigate an end-to-end optimization approach considering future loading outcomes and transportation costs between the pile and load receivers. To predict the evolution of the pile state and the loading performance, we use world models that leverage deep neural networks trained on numerous simulated loading cycles. A look-ahead tree search optimizes the sequence of loading actions by evaluating the performance of thousands of action candidates, which expand into subsequent action candidates under the predicted pile states recursively. Test results demonstrate that, over a horizon of 15 sequential loadings, the look-ahead tree search is 6% more efficient than a greedy strategy, which always selects the action that maximizes the current single loading performance, and 14% more efficient than using a fixed loading controller optimized for the nominal case. Full article

Axially symmetric Taylor–Couette flows of incompressible second grade fluids induced by time-dependent couples inside an infinite circular cylinder are studied under the action of an external magnetic field. The influence of the medium porosity is taken into account in the mathematical modeling. Analytical expressions for the dimensionless non-trivial shear stress and the corresponding fluid velocity were determined using the finite Hankel and Laplace transforms. The solutions obtained are new in the specialized literature and can be customized for various problems of interest in engineering practice. For illustration, the cases of oscillating and constant couples have been considered, and the steady state components of the shear stresses were presented in equivalent forms. Numerical schemes based on finite differences have been formulated for determining the numerical solutions of the proposed problem. It was shown that the numerical results based on analytical solutions and those obtained with the numerical methods have close values with very good accuracy. It was also proved that the fluid flows more slowly and the steady state is reached earlier in the presence of a magnetic field or porous medium. Full article

Audio-driven emotional talking face generation aims to generate talking face videos with rich facial expressions and temporal coherence. Current diffusion model-based approaches predominantly depend on either single-label emotion annotations or external video references, which often struggle to capture the complex relationships between modalities, resulting in less natural emotional expressions. To address these issues, we propose MF-ETalk, a multimodal feature-guided method for emotional talking face generation. Specifically, we design an emotion-aware multimodal feature disentanglement and fusion framework that leverages Action Units (AUs) to disentangle facial expressions and models the nonlinear relationships among AU features using a residual encoder. Furthermore, we introduce a hierarchical multimodal feature fusion module that enables dynamic interactions among audio, visual cues, AUs, and motion dynamics. This module is optimized through global motion modeling, lip synchronization, and expression subspace learning, enabling full-face dynamic generation. Finally, an emotion-consistency constraint module is employed to refine the generated results and ensure the naturalness of expressions. Extensive experiments on the MEAD and HDTF datasets demonstrate that MF-ETalk outperforms state-of-the-art methods in both expression naturalness and lip-sync accuracy. For example, it achieves an FID of 43.052 and E-FID of 2.403 on MEAD, along with strong synchronization performance (LSE-C of 6.781, LSE-D of 7.962), confirming the effectiveness of our approach in producing realistic and emotionally expressive talking face videos. Full article

Small island developing states (SIDS) face a dual constraint of “environmental vulnerability and energy dependence” in the context of climate change. How to achieve just energy transitions has become a core proposition for SIDS to address. This paper focuses on how SIDS can advance Sustainable Development Goal (SDG) 7 (affordable and clean energy) and Sustainable Development Goal 13 (climate action) through UNCLOS–Paris Agreement integration in energy transitions. Grounded in the theoretical framework of the Multidimensional Vulnerability Index (MVI), this research aims to construct a comprehensive analytical system that systematically examines the energy transition challenges facing SIDS and provide multi-level energy transition solutions spanning from international to domestic contexts for climate-vulnerable SIDS. The research findings reveal that SIDS face a structural predicament of “high vulnerability–low resilience” and the triple challenge of “energy–climate–development”. International climate finance is severely mismatched with the degree of vulnerability in SIDS; the United Nations Convention on the Law of the Sea (UNCLOS) and the Paris Agreement lack institutional synergy and fail to adequately support marine renewable energy development in SIDS. In response to these challenges, this study proposes multi-level solutions to promote the synergistic achievement of SDG 7 and SDG 13: at the international level, improve climate finance rules, innovate financing mechanisms, strengthen technological cooperation, and integrate relevant international legal framework; at the domestic level, optimize the layout of marine renewable energy development, construct sustainable investment ecosystems, and strengthen environmental scientific research and local data governance. Full article

Pregnancy and birth are biological processes shaped by social factors, requiring sociological approaches to explain reproductive behaviour. This narrative review outlines the importance of health sociology against the background that health and illness behaviour is influenced by the social environment. The aim of this paper is to summarize the current state of research on the influence of social systems and social milieu behaviour on reproduction, pregnancy, and childbirth in order to make it easier for midwives and doctors to take these factors into account in their everyday clinical and outpatient work. First, the paper lays out the basics of how health and illness are socially constructed, looking at it from both a structural and action-oriented perspective. It then goes on to explain what this means for pregnancy and childbirth as social processes, how women’s health is related to the social construction of gender roles, that breastfeeding is also a social process, and what conclusions can be drawn for the work of midwives. Pregnancy and birth are social processes based on norms and role attributions: “Decisions” regarding one’s own reproductivity are usually only “self-determined” to a limited extent and tend to occur in the context of social norms and milieu-specific role expectations. The promotion of women’s health depends on how milieu-specific norms and logics of action are understood. For all the professions involved in obstetrics, this results in the need for a critical examination of the sociological aspects of health. This implies the necessity for all obstetric professions to critically examine aspects of the sociology of health in order to provide women and their families with appropriate, evidence-based and client-centred care in the context of pregnancy, birth and the postpartum period, against the background of constant social change. Full article

In this study, we discuss a series of eight energy scales, some of which are our own speculations, and fit the logarithms of these energies as a straight line versus a quantity related to the dimensionalities of action terms in a way to be defined in the article. These terms in the action are related to the energy scales in question. So, for example, the dimensionality of the Einstein–Hilbert action coefficient is one related to the Planck scale. In fact, we suppose that, in the cases described with quantum field theory, there is, for each of our energy scales, a pair of associated terms in the Lagrangian density, one “kinetic” and one “mass or current” term. To plot the energy scales, we use the ratio of the dimensionality of, say, the “non-kinetic” term to the dimensionality of the “kinetic” one. For an explanation of our phenomenological finding that the logarithm of the energies depends, as a straight line, on the dimensionality defined integer q, we give an ontological—i.e., it really exists in nature in our model—“fluctuating lattice” with a very broad distribution of, say, the link size a. We take the Gaussian in the logarithm, $ln\left(a\right)$. A fluctuating lattice is very natural in a theory with general relativity, since it corresponds to fluctuations in the gauge depth of the field of general relativity. The lowest on our energy scales are intriguing, as they are not described by quantum field theory like the others but by actions for a single particle or single string, respectively. The string scale fits well with hadronic strings, and the particle scale is presumably the mass scale of Standard Model group monopoles, the bound state of a couple of which might be the dimuon resonance (or statistical fluctuation) found in LHC with a mass of 28 GeV. Full article

The research and application of green fertilizers have long been constrained by financial and technical barriers. Farmers’ adoption of green fertilizers is also highly dependent on government policy support. As an intermediary organization bridging the government and farmers, the STB plays a crucial role in encouraging the use of green fertilizers. In order to explore the impact of the STB’s research and development investment, as well as government intervention on farmers’ green production behavior, this paper constructs a tripartite dynamic game model involving farmers, the STB, and the government. The study systematically analyzes the decision-making mechanisms of the different stakeholders and their evolutionary paths. The results show the following: (1) Under certain conditions, the system converges to two stable states: government withdrawal (1,1,0) and continued government participation (1,1,1). (2) Government intervention shows a phased decrease. As the green fertilizer production system matures, farmers and the STB gradually form a stable collaborative mechanism. At this stage, the government shifts from direct participation to a supervisory role, with its implementation coefficient increasing to between 0.75 and 1, indicating that government supervision becomes the primary mode of action. (3) The research and development efforts of the STB are influenced by both the intensity of government support and technological breakthroughs. During periods of high-intensity government support (with a research and development investment coefficient between 0.05 and 0.15), and when technological accumulation achieves a critical breakthrough, the growth rate of investment increases significantly (the coefficient jumps to 0.15–0.3). (4) Farmers’ demand for green fertilizers is stable and consistent, and the market-oriented collaboration between the STB and farmers tends to favor green production technology, which verifies the feasibility of the government’s withdrawal of functions in the later stage of the green agricultural transformation. This study provides a scientific basis for decision-making regarding the STB’s research and development of green fertilizers, while also laying a theoretical foundation for promoting the green transformation of farmers through green fertilizer innovation. Full article

The growing energy crisis and increasing threat of climate change are driving the need to take action regarding the use of alternative fuels in transport, including public transport. Hydrogen is undoubtedly a fuel which is environmentally friendly and constitutes an alternative to fossil fuels. The wider deployment of hydrogen-powered vehicles involves the need to adapt infrastructure to support the operation of these vehicles. Such infrastructure includes refuelling stations for hydrogen-powered vehicles. The widespread use of hydrogen-powered vehicles is dependent on the development of a network of hydrogen refuelling stations. The aim of this article is to propose the conceptual location of infrastructure for fuelling public transport vehicles with hydrogen in selected cities of the West Pomeranian Voivodeship, in particular the cities of Szczecin and Koszalin. The methodology used to determine the number of refuelling stations is described, and the concept of the location for the refuelling stations has been proposed. Based on a set assumptions, it was stated that two stations may be located in the Voivodeship in 2025 and seven stations in 2040. The research results will be of interest to infrastructure developers, public transport companies, and municipalities involved in making decisions related to the purchase and operation of hydrogen-powered buses. Full article

In existing phased-array radar systems, anti-jamming strategies are mainly generated through manual judgment. However, manually designing or selecting anti-jamming decisions is often difficult and unreliable in complex jamming environments. Therefore, reinforcement learning is applied to anti-jamming decision-making to solve the above problems. However, the existing anti-jamming decision-making models based on reinforcement learning often suffer from problems such as low convergence speeds and low decision-making accuracy. In this paper, a multi-aspect improved deep Q-network (MAI-DQN) is proposed to improve the exploration policy, the network structure, and the training methods of the deep Q-network. In order to solve the problem of the $ϵ$-greedy strategy being highly dependent on hyperparameter settings, and the Q-value being overly influenced by the action in other deep Q-networks, this paper proposes a structure that combines a noisy network, a dueling network, and a double deep Q-network, which incorporates an adaptive exploration policy into the neural network and increases the influence of the state itself on the Q-value. These enhancements enable a highly adaptive exploration strategy and a high-performance network architecture, thereby improving the decision-making accuracy of the model. In order to calculate the target value more accurately during the training process and improve the stability of the parameter update, this paper proposes a training method that combines n-step learning, target soft update, variable learning rate, and gradient clipping. Moreover, a novel variable double-depth priority experience replay (VDDPER) method that more accurately simulates the storage and update mechanism of human memory is used in the MAI-DQN. The VDDPER improves the decision-making accuracy by dynamically adjusting the sample size based on different values of experience during training, enhancing exploration during the early stages of training, and placing greater emphasis on high-value experiences in the later stages. Enhancements to the training method improve the model’s convergence speed. Moreover, a reward function combining signal-level and data-level benefits is proposed to adapt to complex jamming environments, which ensures a high reward convergence speed with fewer computational resources. The findings of a simulation experiment show that the proposed phased-array radar anti-jamming decision-making method based on MAI-DQN can achieve a high convergence speed and high decision-making accuracy in environments where deceptive jamming and suppressive jamming coexist. Full article

This paper seeks to address the limitations of conventional remote sensing data dissemination algorithms, particularly their inability to model fine-grained multi-modal heterogeneous feature correlations and adapt to dynamic network topologies under resource constraints. This paper proposes multi-modal-MAPPO, a novel multi-modal deep reinforcement learning (MDRL) framework designed for a proactive data push in large-scale integrated satellite–terrestrial networks (ISTNs). By integrating satellite cache states, user cache states, and multi-modal data attributes (including imagery, metadata, and temporal request patterns) into a unified Markov decision process (MDP), our approach pioneers the application of the multi-actor-attention-critic with parameter sharing (MAPPO) algorithm to ISTNs push tasks. Central to this framework is a dual-branch actor network architecture that dynamically fuses heterogeneous modalities: a lightweight MobileNet-v3-small backbone extracts semantic features from remote sensing imagery, while parallel branches—a multi-layer perceptron (MLP) for static attributes (e.g., payload specifications, geolocation tags) and a long short-term memory (LSTM) network for temporal user cache patterns—jointly model contextual and historical dependencies. A dynamically weighted attention mechanism further adapts modality-specific contributions to enhance cross-modal correlation modeling in complex, time-varying scenarios. To mitigate the curse of dimensionality in high-dimensional action spaces, we introduce a multi-dimensional discretization strategy that decomposes decisions into hierarchical sub-policies, balancing computational efficiency and decision granularity. Comprehensive experiments against state-of-the-art baselines (MAPPO, MAAC) demonstrate that multi-modal-MAPPO reduces the average content delivery latency by 53.55% and 29.55%, respectively, while improving push hit rates by 0.1718 and 0.4248. These results establish the framework as a scalable and adaptive solution for real-time intelligent data services in next-generation ISTNs, addressing critical challenges in resource-constrained, dynamic satellite–terrestrial environments. Full article

Brazilian propolis is a natural bee product with a unique and diverse chemical composition. It is especially rich in phenols and terpenoids that show a range of significant biological properties. Due to the growing scientific interest, its strong anti-inflammatory and anticancer activity has been highlighted. In vitro and in vivo studies demonstrate its potential to modulate inflammatory pathways by inhibiting pro-inflammatory cytokines, such as tumour necrosis factor (TNF-α) and interleukin 6 (IL-6), as well as by regulating oxidative stress. Additionally, active compounds in Brazilian propolis have the potential to inhibit tumour cell proliferation, induce apoptosis and modulate the tumour microenvironment. Depending on the botanical source and region of occurrence, different types of Brazilian propolis are distinguished, including green, red and brown, which differ in composition and biological activity. Green propolis, rich in artepilin C and phenolic acids, shows strong anti-inflammatory and anticancer properties. Red propolis contains isoflavones and quercetin that enhance its antioxidant and immunomodulatory activities. Brown propolis, rich in cinnamic acids and benzophenones, exerts cytotoxic effects against certain lines of cancer cells. This article discusses the current state of knowledge on the mechanisms of action of different types of Brazilian propolis and their potential uses as supportive therapy in inflammatory and cancerous diseases in combination with nanotechnology. Full article

Background/Objectives: Sickle cell disease (SCD) is caused by a β-globin gene mutation (βGlu6Val) that produces sickle hemoglobin (HbS). When deoxygenated, HbS polymerizes, leading to red blood cell (RBC) sickling; therefore, hemoglobin is a central therapeutic target for SCD. Current strategies include increasing the levels of oxygenated HbS (which cannot polymerize) and/or directly destabilizing the deoxygenated HbS polymer. This study aimed to design and synthesize next-generation Michael acceptor antisickling hemoglobin modifiers (MMA-206, MMA-207, MMA-208, and MMA-209) and evaluate their antisickling efficacy. Methods: Four Michael acceptor compounds (MMA-206 to MMA-209) were synthesized and characterized. Their pharmacologic activities and modes of action were assessed in vitro using disulfide exchange reaction with normal hemoglobin, sickling inhibition assays with sickle red blood cells, and hemoglobin oxygen equilibrium curve analysis with normal and sickle red blood cells. Results: MMA-206 exhibited the strongest antisickling activity, outperforming previously studied Michael acceptor antisickling agents. All four MMA analogues bound to hemoglobin at βCys93, destabilizing the low-oxygen-affinity T-state and thereby preventing deoxygenation-induced HbS polymerization and RBC sickling. In addition, they appeared to directly destabilize the HbS polymer, indicating a second mechanism of action. Furthermore, time-dependent oxygen equilibrium measurements confirmed that their pharmacologic effect was sustained over time in vitro. Conclusions: The new Michael acceptor compounds, particularly MMA-206, demonstrated potent antisickling effects via dual mechanisms and showed sustained activity. These findings highlight Michael acceptor compounds’ promise as hemoglobin oxygen-affinity modulators for the treatment of SCD. Full article