Search Results (6,503)

In this paper, an integrated geometric and thermal-induced errors prediction approach for active error compensation in machine tools is proposed and evaluated. The proposed approach is based on a hybrid of physical and neural network predictive modeling to drive an adaptive position controller for real-time error compensation including geometric and thermal-induced errors. Error components are formulated as a three-dimensional error field in the time-space domain. This approach involves four key steps for its development and implementation: (i) simplified experimental procedure combining a multicomponent laser interferometer measurement system and sixteen thermal sensors for error components measurement, (ii) artificial neural network-based predictive modeling of both position-dependent and position-independent error components, (iii) tridimensional volumetric error mapping using rigid body kinematics, and finally (iv) implementation of the real-time error compensation. Assessed on a turning center, the proposed approach conducts a significant improvement of the machine accuracy. The maximum error is reduced from 30 µm to less than 3 µm under thermally varying conditions. Full article

Seafood-derived carbonate waste, primarily calcium carbonate (CaCO₃), has attracted growing interest for sustainable reuse, yet the unique potential of aged biogenic sources remains underexplored. Blue crab (Callinectes sapidus) shells are particularly distinctive: they consist of Mg-calcite with an intrinsic 3D-porous structure and naturally embedded astaxanthin, a potent antioxidant not found in other calcite- or aragonite-based residues. While organic degradation over time is often assumed to compromise functionality, this study demonstrates that five-years-aged crustacean shell waste retains both its crystallinity and bioactive carotenoids after calibrated ball milling. Across four powder batches produced under distinct milling conditions by varying frequencies and durations, dynamic light scattering confirmed only subtle particle size variation, while Raman spectroscopy, XRD, FT-IR, and SEM-EDX confirmed structural and chemical integrity and highlighted the subtle amorphization induced by slightly different milling parameters, which, in turn, driven to slightly different conversion efficiency into phosphate mineral. Strikingly, all powders underwent rapid transformation into dicalcium phosphate dihydrate (brushite) enriched with carotenoids upon reaction with phosphoric acid. This work reveals, for the first time, that years-aged biogenic Mg-calcite waste not only preserves its naturally embedded carotenoids but also offers a direct route to functional phosphate composites, establishing its untapped value in environmental and biomedical applications. Full article

The growing world population is putting pressure on food and feed production. For many years, selenium deficiencies have been observed in the diets of the inhabitants of most European and other continental countries, both in the environment and in food and fodder. Therefore, it is becoming necessary to supplement these deficiencies. A 3-year field study was therefore carried out to determine the effect of sulphur (0, 15 and 30 kg S ha⁻¹) and selenium (0, 10 and 20 g Se ha⁻¹) on the yield and biometric traits of winter forms of spelt wheat and common wheat, as well as the timing of application (at the tillering stage, BBCH 22–24, and the stem-shooting stage, BBCH 31–34). Sulphur fertilisation had a slight but positive effect on both the grain and straw yields of both wheat species, especially spelt wheat. The highest increase in spelt wheat grain and straw yield and common wheat straw yield was obtained after applying sulphur at a dose of 15 kg S ha⁻¹, by 3%, 5% and 5%, respectively. In the case of selenium, a higher dose (20 g Se ha⁻¹) had the most beneficial effect on the grain yield of spelt wheat (5% increase) and common wheat (8% increase). In turn, a lower dose of this element (10 g Se ha⁻¹) contributed to an increase in the straw yield of both wheat species, by 10% and 17%, respectively. The yield of spelt and common wheat was not dependent on the timing of Se application. The beneficial effect of S and Se fertilisation on the growth and development of the tested plants is also indicated by the high (exceeding 1) tolerance index for Se and the yield response for S. The effect of S and Se on the weight of a thousand grains was not clear-cut. The density of spelt and common wheat ears increased as a result of the impact of S and Se (S: by 6% and 5%; Se: by 10% and 15%, respectively). Delaying the application of Se contributed to an increase in the density of the tested plants. Full article

Accurate identification of rear light signals in preceding vehicles is pivotal for Advanced Driver Assistance Systems (ADAS), enabling early detection of driver intentions and thereby improving road safety. In this work, we present a novel approach that leverages a meta-learning-enhanced YOLOv8 model to detect left and right turn indicators, as well as brake signals. Traditional radar and LiDAR provide robust geometry, range, and motion cues that can indirectly suggest driver intent (e.g., deceleration or lane drift). However, they do not directly interpret color-coded rear signals, which limits early intent recognition from the taillights. We therefore focus on a camera-based approach that complements ranging sensors by decoding color and spatial patterns in rear lights. This approach to detecting vehicle signals poses additional challenges due to factors such as high reflectivity and the subtle visual differences between directional indicators. We address these by training a YOLOv8 model with a meta-learning strategy, thus enhancing its capability to learn from minimal data and rapidly adapt to new scenarios. Furthermore, we developed a post-processing layer that classifies signals by the geometric properties of detected objects, employing mathematical principles such as distance, area calculation, and Intersection over Union (IoU) metrics. Our approach increases adaptability and performance compared to traditional deep learning techniques, supporting the conclusion that integrating meta-learning into real-time object detection frameworks provides a scalable and robust solution for intelligent vehicle perception, significantly enhancing situational awareness and road safety through reliable prediction of vehicular behavior. Full article

In Japan, where competitive entrance exams are widespread, many students experience unwilling enrollment, entering a university that they do not wish to attend. This can hinder adjustment and increase the risk of dropping out, making support for academic self-direction essential. This study empirically examined a model of how students develop self-direction. A survey was conducted with 336 individuals who had graduated within the past five years and experienced unwilling enrollment. Based on their reasons, participants were classified into two groups: those unwillingly enrolled yet intending to pursue higher education (n = 241), and those unwillingly enrolled owing to a lack of intent to pursue higher education (n = 95). Multi-group structural equation modeling showed that “Trusting Relationships with Others” and “Having Time and Space for Self-Reflection” promoted both “Discovering Personal Meaning in the Enrolled University” and “Clarification of Career Goals,” which in turn fostered “Development of Self-Direction.” Additionally, for those who intended to pursue higher education, “Realization of Experiences Unique to the Enrolled University” played a greater role, while for those lacking such intent, “Clarification of Career Goals” was more influential. These results suggest that tailored support, aligned with students’ reasons for unwillingness, is the key to fostering their self-direction. Full article

The ubiquitous environmental pollution with micro- and nano-sized plastic particles (MNPs) is a current and significant problem today. At the same time, lung cancer is responsible for the largest number of cancer-related deaths worldwide. Many research groups have investigated the relationship between lung cancer development and exposure to MNPs in recent years. Studies have demonstrated that these particles could enter the respiratory system in a variety of ways—both directly through inhaled air and through the bloodstream, and through internalization in the intestines and other digestive organs. Data regarding the possibility of their aggregation in the respiratory system, thyroid gland, and brain are also concerning, as the harmful effects of MNPs have been proven to depend on their concentration and exposure time. The primary response of cells to plastic particles is an increase in oxidative stress. This is generated both by the cell itself (especially macrophages) and induced by damage caused by mechanical damage to cellular organelles by MNPs. The consequences of MNP exposure can include metabolic disturbances, DNA damage, and mutations, ultimately inducing neoplastic transformation in healthy cells. This can lead to changes in tissue architecture and increase their susceptibility to other pathogens, such as pathogenic microorganisms or heavy metals. These, in turn, can be internalized along with MNPs, forming a corona surrounding them. Full article

Gestational Diabetes Mellitus (GDM) is a metabolic condition characterized by glucose intolerance, which manifests or is diagnosed for the first time during pregnancy. Hyperglycemia associated with GDM can induce a systemic and local inflammatory environment, directly affecting the maternal–fetal interface, particularly the placenta. The placenta, in turn, plays a central role in immune modulation and can alter cytokine and immune cell expression in response to metabolic stress. This study aimed to evaluate levels of inflammatory cytokines and the profiles of type 1 (M1) and type 2 (M2) macrophages in placentas from pregnant women with GDM. Forty placental samples were analyzed and divided into two groups: pregnant women with GDM (n = 20) and normoglycemic pregnant women (n = 20). The villous and extravillous portions were separated and analyzed for cytokine levels by flow cytometry and for macrophage immunophenotyping. The results showed a significant increase in IL-6, IL-8, IL-10, and IL-12P70 levels in the placentas of mothers with GDM, whereas IL-1β and TNF-α were reduced in the extravillous portion of this group. In addition, a higher percentage of CD14+ cells and M2 macrophages was observed, especially in the villous portion of the placentas of pregnant women with GDM. These findings suggest that gestational hyperglycemia modulates the placental immune response, altering cytokine levels and macrophage polarization patterns. GDM influences the placental immunological microenvironment, which can contribute to alterations in placental function and increased risks to fetal development. The data underscore the placenta’s role as an immunoregulatory organ and highlight the need for greater attention to inflammation associated with GDM in maternal and child health. Full article

This study addresses the challenge of low-temperature synthesis of the high-performance L1₀ Fe-Pt intermetallic phase, which is critical for applications in ultra-high-density data storage and advanced magnetic devices. We demonstrate that the choice of iron precursor is a decisive factor in directing the phase composition and thermal evolution of Fe-Pt nanostructures, ultimately determining their suitability as functional composite materials. Fe-Pt systems were synthesized from aqueous solutions using platinum(IV) chloric acid (H₂PtCl₆) with either iron(III) ammonium sulfate (NH₄Fe(SO₄)₂) or iron(II) sulfate (FeSO₄). Comprehensive characterization using X-ray diffraction and high-resolution transmission electron microscopy revealed distinct composite formations. The iron(III) precursor yielded homogeneous, thermally stable nanocomposites: as-synthesized nanoparticles formed a Pt-based FCC solid solution (~5 nm), which upon annealing at 500 °C transformed into a biphasic nanocomposite of FCC solid solution and an L1₂ Fe₂₁Pt₇₉ intermetallic phase with minimal grain growth (~7 nm). In stark contrast, the system derived from iron(II) sulfate resulted in a heterogeneous composite of 4 nm Pt nanoparticles, an FCC solid solution, and discrete 1–3 nm Fe nanoparticles with L1₂-ordered FePt₃ domains. Annealing this heterogeneous mixture caused phase segregation, forming significantly coarsened Pt-rich crystals (~30 nm) that were approximately 4–6 times larger than the crystallites in the annealed homogeneous composite, with negligible Fe incorporation. Our findings establish that precursor chemistry dictates the initial nanocomposite architecture, which in turn controls the pathway and success of low-temperature intermetallic phase formation. This work provides a crucial design principle for fabricating tailored Fe-Pt composite nanomaterials, moving beyond simple alloys to engineered multiphase systems for practical application. Full article

Sampling-based algorithms are pivotal for high-dimensional UAV path planning, especially in 3D urban environments. The Rapidly-Exploring Random Tree (RRT) suffers from inadequate sampling methods and a single, fixed sampling policy, which lead to elongated paths and higher computational cost. To address this, we propose a Dynamic Adaptive DACS-RRT* algorithm that builds a dynamic, bidirectional sampling space and fuses low-discrepancy Halton sampling with bridge (narrow-passage) sampling, fundamentally tailoring the sampling process to urban settings. We further construct an adaptive, coordinated sampling strategy that dynamically adjusts between straight-to-goal and frustum-cone expansions by computing their probabilities, thereby overcoming the limitations of a single strategy and strengthening directional guidance. After generating a path, we perform multi-objective smoothing to make UAV trajectories better suited to urban environments. Through simulations in three distinct urban scenarios—and in comparison with five baseline algorithms—DACS-RRT* shows improvements in path length, convergence time, node count, iteration count, obstacle clearance, and turning angle, further validating its practicality in urban settings. Full article

Generative artificial intelligence (AI) is reshaping creative practices, yet many systems rely on traditional interfaces, limiting intuitive and embodied engagement. This study presents a qualitative observational analysis of participant interactions with a real-time generative AI installation designed to co-create Ukiyo-e-style artwork through embodied inputs. The system dynamically interprets physical presence, object manipulation, body poses, and gestures to influence AI-generated visuals displayed on a large public screen. Drawing on systematic video analysis and detailed interaction logs across 13 sessions, the research identifies core modalities of interaction, patterns of co-creation, and user responses. Tangible objects with salient visual features such as color and pattern emerged as the primary, most intuitive input method, while bodily poses and hand gestures served as compositional modifiers. The system’s immediate feedback loop enabled rapid learning and iterative exploration and enhanced the user’s feeling of control. Users engaged in collaborative discovery, turn-taking, and shared authorship, frequently expressing a positive effect. The findings highlight how embodied interaction lowers cognitive barriers, enhances engagement, and supports meaningful human–AI collaboration. This study offers design implications for future creative AI systems, emphasizing accessibility, playful exploration, and cultural resonance, with the potential to democratize artistic expression and foster deeper public engagement with digital cultural heritage. Full article

This paper presents a novel Ripple Evolution Optimizer (REO) that incorporates adaptive and diversified movement—a population-based metaheuristic that turns a coastal-dynamics metaphor into principled search operators. REO augments a JADE-style current-to-p-best/1 core with jDE self-adaptation and three complementary motions: (i) a rank-aware that pulls candidates toward the best, (ii) a time-increasing that aligns agents with an elite mean, and (iii) a scale-aware sinusoidal that lead solutions with a decaying envelope; rare Lévy-flight kicks enable long escapes. A reflection/clamp rule preserves step direction while enforcing bound feasibility. On the CEC2022 single-objective suite (12 functions spanning unimodal, rotated multimodal, hybrid, and composition categories), REO attains 10 wins and 2 ties, never ranking below first among 34 state-of-the-art compared optimizers, with rapid early descent and stable late refinement. Population-size studies reveal predictable robustness gains for larger N. On constrained engineering designs, REO achieves outperforming results on Welded Beam, Spring Design, Three-Bar Truss, Cantilever Stepped Beam, and 10-Bar Planar Truss. Altogether, REO couples adaptive guidance with diversified perturbations in a compact, transparent optimizer that is competitive on rugged benchmarks and transfers effectively to real engineering problems. Full article

Over the last few decades, the item recognition problem has been mostly addressed through radar techniques or computer vision algorithms. While signal/image processing has mainly fueled the recognition process in the past, machine/deep learning methods have recently stepped in, to the extent that they nowadays represent the state-of-the-art methodology. In particular, Convolutional Neural Networks are spreading worldwide as effective tools for image-based object recognition. Nevertheless, the images used to feed vision-based algorithms may not be available in some cases, and/or may have poor quality. Furthermore, they can also pose privacy issues. For these reasons, this paper investigates a novel machine learning object recognition approach based on electromagnetic backscattering in the frequency domain. In particular, a 1D Convolutional Neural Network is employed to map the collected, backscattered signals onto two classes of objects. The experimental framework is aimed at data collection through backscattering measurements in the mmWave band with signal generators and spectrum analyzers in controlled environments to ensure data reliability. Results show that the proposed method achieves 100% accuracy in object detection and 84% accuracy in object recognition. This performance makes electromagnetic-based object recognition systems a possible solution to complement vision-based techniques, or even to replace them when they turn out impractical. The findings also reveal a trade-off between accuracy and processing speed when varying signal bandwidths and frequency steps, making this approach flexible and possibly suitable for real-time applications. Full article

Coming from the “end of the world”, from the south of the planet, Pope Francis first challenged global consciousness with his Encyclical Letter Laudato Si’: On Care for Our Common Home, then turned the attention of the Church and people of good will to the Amazon region. The convening of the Synod of Bishops for the Pan-Amazon Region was an initiative deeply attuned to the climate crisis, one of the most pressing challenges of our time. Faithful to the Second Vatican Council and the spirituality of liberation, Francis invites the whole world to admire and recognize the Amazon region as a sacred mystery as well as to heed the voices of its poor communities, precisely those whose resistance has preserved the rainforest. Using an exploratory bibliographical methodology, this article aims to contribute to the reflection on how 21st-century Liberation Theology might address challenges, with an emphasis on the ecological crisis central to Pope Francis’s magisterium, particularly articulated in his Post-Synodal Apostolic Exhortation Querida Amazonia: To the People of God and to All Persons of Good Will. As a result, we tried to demonstrate that Francis, in Querida Amazonia, proposes that a Church with an Amazonian face, located in the heart of Latin America, without forgetting the feminine protagonism, should be an inspiration for integral conversion on the planet. Full article

This study constructs a Stackelberg differential game model for green technology invest-ment in the food industry under a governmental coordination mechanism. The optimal dynamic strategies for local governments and enterprises are derived using Pontryagin’s maximum principle. The backward differential equation method is employed in this study. It is used to analyze the impact of shadow prices on the optimal decisions of both parties. Furthermore, the study examines how social welfare benefits influence the food quality levels within the jurisdiction of local governments. Based on these findings, optimal strategy pathways are proposed to achieve social welfare and enterprise profit maximization in the green transition process of both government and enterprises. The results indicate that a local government’s investment in food quality improvement significantly enhances the food quality levels within their jurisdictions—greater government investment leads to higher food quality. At the same time, food quality levels are positively correlated with the enterprises’ green technology capital investment. Additionally, consumer price sensitivity and sensitivity to price differences have a notable impact on product pricing. As consumers become more price-sensitive, product prices decrease accordingly, which, in turn, helps increase the market share of the enterprises’ products. Full article

Background/Objectives: Here, we report the design, synthesis, and in vitro biological evaluation of a novel stimuli-sensitive nanotherapeutics based on cisplatin analog, cis-[PtCl₂(NH₃)(2-(3-oxobutyl)pyridine)] (Pt-OBP), covalently linked to a N-(2-hydroxypropyl)methacrylamide (HPMA) copolymer via a pH-sensitive hydrazone bond. Methods: Two polymer–drug conjugates, P-Pt-A and P-Pt-B, were synthesized, differing in spacer length between the polymer chain and hydrazone bond, which in turn modulates their drug release kinetics. Results: The spacer based on hydrazone bond demonstrated satisfactory stability under blood-mimicking conditions while enabling selective release of the active drug intracellularly or even in the mildly acidic tumor microenvironment. Pt-OBP exhibits comparable or even superior cytostatic and cytotoxic activity to carboplatin across a panel of murine and human cancer cell lines, with the highest potency observed in FaDu cells representing human head and neck squamous cell carcinoma. Mechanistically, Pt-OBP induced significant phosphorylation of γ-H2AX and activation of caspase-3, indicating its ability to cause DNA damage with subsequent apoptosis induction. P-Pt-A retained moderate biological activity, whereas the slower-releasing P-Pt-B exhibited reduced potency in vitro, consistent with its drug release profile. Conclusions: Notably, free Pt-OBP induced rapid apoptotic cell death, surpassing carboplatin at early time points, and the polymeric conjugates achieved comparable pro-apoptotic activity after extended incubation, suggesting effective intracellular release of the active drug. Full article