Search Results (6,147)

Maize leaf area offers valuable insights into physiological processes, playing a critical role in breeding and guiding agricultural practices. The Azure Kinect DK possesses the real-time capability to capture and analyze the spatial structural features of crops. However, its further application in maize leaf area measurement is constrained by RGB–depth misalignment and limited sensitivity to detailed organ-level features. This study proposed a novel approach to address and optimize the limitations of the Azure Kinect DK through the multimodal coupling of RGB-D data for enhanced organ-level crop phenotyping. To correct RGB–depth misalignment, a unified recalibration method was developed to ensure accurate alignment between RGB and depth data. Furthermore, a semantic information-guided depth inpainting method was proposed, designed to repair void and flying pixels commonly observed in Azure Kinect DK outputs. The semantic information was extracted using a joint YOLOv11-SAM2 model, which utilizes supervised object recognition prompts and advanced visual large models to achieve precise RGB image semantic parsing with minimal manual input. An efficient pixel filter-based depth inpainting algorithm was then designed to inpaint void and flying pixels and restore consistent, high-confidence depth values within semantic regions. A validation of this approach through leaf area measurements in practical maize field applications—challenged by a limited workspace, constrained viewpoints, and environmental variability—demonstrated near-laboratory precision, achieving an MAPE of 6.549%, RMSE of 4.114 cm², MAE of 2.980 cm², and R² of 0.976 across 60 maize leaf samples. By focusing processing efforts on the image level rather than directly on 3D point clouds, this approach markedly enhanced both efficiency and accuracy with the sufficient utilization of the Azure Kinect DK, making it a promising solution for high-throughput 3D crop phenotyping. Full article

Rudolf Fuchs (1890–1942) was an influential figure in Prague’s early 20th-century literary scene as well as an intermediary between German and Czech literature at a time of intense social and political transformation. This study places Fuchs’ work within the broader context of his period, underscoring his significant, yet often overlooked, contributions to cultural mediation. The methods employed in this study include a detailed examination of Fuchs’ literary output and translations, alongside an analysis of his correspondence with contemporaries, to trace the evolution of his thinking from an initial alignment with Expressionism to a strong commitment to socialism. The results highlight Fuchs’ role in enhancing intercultural understanding, revealing how his translations and personal ideologies shaped the literary landscape of German–Czech relations. The study asserts the enduring relevance of Fuchs’ endeavors, particularly in the modern European sociopolitical climate. It also yields insights into the management of cultural diversity and ideological conflicts, as well as the upholding of social values amidst political volatility. Full article

The purpose of this study is to analyze the mission statements of 49 companies listed on the Palestine Exchange (PEX), focusing on their structure and alignment with environmental, social, and governance (ESG) principles. The primary objective of this research was to evaluate the extent to which Palestinian companies embed sustainability issues into their mission statements. This was done with a qualitative research design and a descriptive content analysis method, letting mission statements from different fields be looked at in excellent detail. This analysis offers valuable insights into how Palestinian companies articulate their strategic goals and communicate their commitment to ESG factors. The findings reveal that Palestinian companies demonstrate a clear understanding of sustainability and its relevance to their operations. A lot of companies are also working hard to include sustainability principles in their mission statements. This shows that people are becoming more aware of how important ESG factors are for shaping business strategy and creating long-term value. Full article

The metal bipolar plate is a critical component of the hydrogen fuel cell stack used in proton exchange membrane fuel cells. Bipolar plates must have high accuracy micro-channels with a high aspect ratio (AR) between the channel depth and the half periodic width to achieve optimal cell performance. Conventional forming methods, such as micro-stamping, hydroforming, and rubber pad forming, cannot achieve these high ARs given that in these processes, material deformation is dominated by stretch deformation. In micro-roll forming the major deformation mode is bending, and this enables production of channels with higher ARs than is currently possible. However, micro-roll forming uses multiple sets of forming roll stands to form the part and this leads to technological challenges related to tool alignment and roll tool precision that must be overcome before widespread application can be achieved. This study presents a new methodology to achieve tight tool tolerances when producing micro-roll tooling by utilizing wire-EDM and micro-turning techniques. This is combined with a new micro-roll former design that enables high-precision tool alignment across multiple roll stations. Proof of concept is provided through micro-roll forming trials performed on ultra-thin titanium sheets that show that the proposed technology can achieve tight dimensional tolerances in the sub-millimeter scale that suits bipolar plate applications. Full article

Introduction: NMR spectroscopy is a powerful technique for studying metabolism, either in metabolomics settings or through tracing with stable isotope-enriched metabolic precursors. MetaboLabPy (version 0.9.66) is a free and open-source software package used to process 1D- and 2D-NMR spectra. The software implements a complete workflow for NMR data pre-processing to prepare a series of 1D-NMR spectra for multi-variate statistical data analysis. This includes a choice of algorithms for automated phase correction, segmental alignment, spectral scaling, variance stabilisation, export to various software platforms, and analysis of metabolic tracing data. The software has an integrated help system with tutorials that demonstrate standard workflows and explain the capabilities of MetaboLabPy. Materials and Methods: The software is implemented in Python and uses numerous Python toolboxes, such as numpy, scipy, pandas, etc. The software is implemented in three different packages: metabolabpy, qtmetabolabpy, and metabolabpytools. The metabolabpy package contains classes to handle NMR data and all the numerical routines necessary to process and pre-process 1D NMR data and perform multiplet analysis on 2D-¹H, ¹³C HSQC NMR data. The qtmetabolabpy package contains routines related to the graphical user interface. Results: PySide6 is used to produce a modern and user-friendly graphical user interface. The metabolabpytools package contains routines which are not specific to just handling NMR data, for example, routines to derive isotopomer distributions from the combination of NMR multiplet and GC-MS data. A deep-learning approach for the latter is currently under development. MetaboLabPy is available via the Python Package Index or via GitHub. Full article

Fresh produce contamination poses a significant public health risk. Traditional disinfection methods using chemical solutions, while effective, raise environmental and health concerns. This study explores UVC irradiation, a promising non-chemical alternative proven to be effective against a broad spectrum of microorganisms. We investigated the optimal UVC dosage for reducing microorganisms on fresh vegetables washed in water. Our findings suggest that dosages of approximately 2 mJ/cm² in water and 9 mJ/cm² in vegetables achieve reductions of up to 99%. Additionally, we established a nominal radiation application rate of 2.38 mW/cm²/s, reflecting the treatment intensity. Understanding the fundamental mechanisms of UVC irradiation and its interactions with microorganisms is crucial. Elucidating these mechanisms can significantly improve optimization efforts and seamlessly integrate UVC irradiation into food safety protocols. Implementing this strategy offers immense potential to elevate food safety standards in the industry while minimizing environmental impact. This approach aligns perfectly with sustainability objectives by providing a chemical-free solution for food disinfection. Full article

The perovskite oxides CaMnO_3-δ, Ca_0.5Sr_0.5MnO_3-δ, and SrMnO_3-δ were synthesized in air using a solid-state method, and their structural, electrical, and electrocatalytic properties were studied in relation to their oxygen evolution reaction (OER) performance. Iodometric titration showed δ values of 0.05, 0.05, and 0.0, respectively, indicating that Mn is predominantly in the 4+ oxidation state across all materials, consistent with prior reports. Detailed characterization was performed using X-ray diffraction (XRD), scanning electron microscopy (SEM), iodometric titration, and variable-temperature conductivity measurements. Four-point probe DC measurements revealed that CaMnO_3-δ (δ = 0.05) has a semiconductive behavior over a temperature range from 25 °C to 300 °C, with its highest conductivity attributed to polaron activity. Cyclic voltammetry (CV) in 0.1 M KOH was employed to assess OER catalytic performance, which correlated with room-temperature conductivity. CaMnO_3-δ exhibited superior catalytic activity, followed by Ca_0.5Sr_0.5MnO_3-δ and SrMnO_3-δ, demonstrating that increased conductivity enhances OER performance. The conductivity trend, CaMnO_3-δ > Ca_0.5Sr_0.5MnO_3-δ > SrMnO_3-δ, aligns with OER activity, underscoring a direct link between electronic transport properties and catalytic efficiency within this series. Full article

In natural environments, fish almost always swim in groups. Investigating the coupled mechanism of biomimetic fish exhibiting autonomous swimming capabilities advances our understanding of fish schooling phenomena and simultaneously aids in refining the structural and formation configurations of underwater robotic vehicles. This work innovatively develops an algorithm based on the Direct-Forcing Immersed Boundary Method (DF-IBM) and implements it in an efficient, modular software program written in C++. The program accelerates the calculation process by using a multigrid method. Validation against a benchmark case of flow around a cylinder, with comparison to data from the existing literature, verifies the program’s precision with discrepancies of less than 3.6%. Based on this algorithm, the paper analyzes the incompressible viscous flow during the movement of parallel-aligned biomimetic fish. It uncovers the interaction between the fish’s motion and the surrounding flow field and also reveals the hydrodynamic mechanisms of the group motion of the parallel-aligned biomimetic fish. The flow field under varying spacing and phases between the parallel-aligned biomimetic fish proves that the interaction between the flow fields induced by the two fish bodies becomes increasingly significant when decreasing the lateral spacing from $1.4L$ to $0.6L$. Notably, an initial lateral convergence of the fish bodies is observed, followed by a sideways swimming pattern at a particular pitch angle, accompanied by a decrement in their forward swimming velocity as they approach each other. Additionally, this study compares flow field alterations in parallel-aligned biomimetic fish with identical lateral spacing but opposing flapping phases. The findings indicate that, irrespective of the phase, the fish exhibit an initial convergence followed by a sideways motion at a specific pitch angle. However, due to disparities in the tail’s flow field, a larger pitch angle is generated when the fish swim in unison. All the findings above will provide a solid theoretical foundation for the design and optimization of underwater robotic vehicles. Full article

Background: Postural changes are considered a public health issue and have gathered significant interest in both research and clinical practice. Aims: To evaluate the effectiveness of Global Postural Reeducation (GPR) in improving postural changes and postural stability in healthy young adults. Additionally, this study aims to identify the main postural changes in the sample population. Methods: A longitudinal study was conducted with a sample of students (n = 38) from the 2nd and 3rd years of undergraduate programs at Coimbra Health School, divided into an experimental group (EG) with 20 subjects and a control group (CG) with 18 subjects. The EG underwent a GPR intervention, while the CG received no intervention. Postural changes were assessed using a 3D motion analysis system (Qualisys), and stabilometry was evaluated using a Bertec force platform. Results: At baseline (T0), the groups were homogeneous regarding sample characterization variables, as well as postural and stabilometric variables (p > 0.05). After four weeks of the intervention (T1), no significant differences were observed between the EG and CG for any of the variables studied (p > 0.05). However, within-group analysis for the experimental group revealed a significant difference (p = 0.04) in anterior-posterior velocity, indicating a reduction in this parameter from T0 to T1. In the control group, a significant difference was observed (p = 0.03) in the left knee valgus, indicating a reduction in valgus alignment. Conclusions: GPR does not appear to be effective in improving postural changes or center of pressure displacement in healthy young students. Full article

Generating high-quality programming exercises with well-aligned problem descriptions, test cases, and code solutions is crucial for computer science education. However, current methods often lack coherence among these components, reducing their educational value. We present CodeContrast, a novel generative model that uses contrastive learning to map programming problems, test cases, and solutions into a shared feature space. By minimizing the distance between matched components and maximizing it for non-matched ones, CodeContrast learns the intricate relationships necessary to generate coherent programming exercises. Our model architecture includes three encoder networks for problem descriptions, test cases, and solutions. During training, CodeContrast processes positive triplets (matching problem, test case, solution) and negative triplets (non-matching combinations) and uses a contrastive loss to position positive triplets close in the feature space while separating negative ones. Comprehensive evaluations of CodeContrast—through automatic metrics, expert ratings, and student studies—demonstrate its effectiveness. Results show high code correctness (92.3% of test cases passed), strong problem–solution alignment (BLEU score up to 0.826), and robust test case coverage (85.7% statement coverage). Expert feedback and student performance further support the pedagogical value of these generated exercises, with students performing comparably to those using manually curated content. CodeContrast advances the automated generation of high-quality programming exercises, capturing relationships among programming components to enhance educational content and improve the learning experience for students and instructors. Full article

Pigment production has a substantial negative impact on the environment, since mining for natural pigments causes ecosystem degradation, while synthetic pigments, derived from petrochemicals, generate toxic by-products that accumulate and persist in aquatic systems due to their resistance to biodegradation. Despite these challenges, pigments remain essential across numerous industries, including the cosmetic, textile, food, automotive, paints and coatings, plastics, and packaging industries. In response to growing consumer demand for sustainable options, there is increasing interest in eco-friendly alternatives, particularly bio-based pigments derived from algae, fungi, and actinomycetes. This shift is largely driven by consumer demand for sustainable options. For bio-pigments, actinomycetes, particularly from the Streptomyces genus, have emerged as a promising green source, aligning with global sustainability goals due to their renewability and biodegradability. Scale-up of production and yield optimization challenges have been circumvented with the aid of biotechnology advancements, including genetic engineering and innovative fermentation and extraction methods, which have enhanced these bio-pigments’ viability and cost-competitiveness. Actinomycete-derived pigments have successfully transitioned from laboratory research to commercialization, showcasing their potential as sustainable and eco-friendly alternatives to synthetic dyes. With the global pigment market valued at approximately USD 24.28 billion in 2023, which is projected to reach USD 36.58 billion by 2030, the economic potential for actinomycete pigments is extensive. This review explores the environmental advantages of actinomycete pigments, their role in modern industry, and the regulatory and commercialization challenges they face, highlighting the importance of these pigments as promising solutions to reduce our reliance on conventional toxic pigments. The successful commercialization of actinomycete pigments can drive an industry-wide transition to environmentally responsible alternatives, offering substantial benefits for human health, safety, and environmental sustainability. Full article

This work proposes multi-objective two-stage distribution optimal power flow (D-OPF) to coordinate the use of smart inverters (SIs) and existing voltage control legacy devices. The first stage of multi-objective D-OPF aims to solve a mixed-integer nonlinear programming (MINLP) formulation that minimizes both voltage variation and active power loss, with SI modes, SI settings, voltage regulator (VR) taps, and capacitor bank (CB) status as control variables. The Pareto Optimal Solutions obtained from the first-stage MINLP are used to determine the optimal active–reactive power dispatch from the SIs by solving a nonlinear programming formulation in the second stage of the proposed D-OPF. This model guarantees that the setpoints for active–reactive power align with the droop characteristics of the SIs, ensuring practicability and the autonomous dispatch of active–reactive power by the SIs according to IEEE 1547-2018. The effectiveness of the proposed method is tested on the IEEE 123 distribution network by contrasting the two proposed D-OPF models, with one prioritizing SIs for voltage control and power loss minimization and the other not prioritizing SIs. The simulation results demonstrate that prioritizing SIs with optimal mode and droop settings can improve voltage control and power loss minimization. The proposed model (with SI prioritization) also reduces the usage of traditional grid control devices and optimizes the dispatch of active–reactive power. The POS also shows that the SI modes, droops, and legacy device settings can be effectively obtained based on the desired objective priority. Full article

In grid intelligent inspection systems, automatic registration of infrared and visible light images in power scenes is a crucial research technology. Since there are obvious differences in key attributes between visible and infrared images, direct alignment is often difficult to achieve the expected results. To overcome the high difficulty of aligning infrared and visible light images, an image alignment method is proposed in this paper. First, we use the Sobel operator to extract the edge information of the image pair. Second, the feature points in the edges are recognised by a curvature scale space (CSS) corner detector. Third, the Histogram of Orientation Gradients (HOG) is extracted as the gradient distribution characteristics of the feature points, which are normalised with the Scale Invariant Feature Transform (SIFT) algorithm to form feature descriptors. Finally, initial matching and accurate matching are achieved by the improved fast approximate nearest-neighbour matching method and adaptive thresholding, respectively. Experiments show that this method can robustly match the feature points of image pairs under rotation, scale, and viewpoint differences, and achieves excellent matching results. Full article

Haberman linking is a widely used method for comparing groups using the two-parameter logistic item response model. However, the traditional Haberman linking approach relies on joint item parameter estimation, which prevents the application of standard M-estimation theory for linking error calculation in the presence of differential item functioning. To address this limitation, a novel pairwise Haberman linking method is introduced. Pairwise Haberman linking aligns with Haberman linking when no items are missing but eliminates the need for joint item parameters, allowing for the use of M-estimation theory in linking error computation. Theoretical derivations and simulation studies show that pairwise Haberman linking delivers reliable statistical inferences for items and persons, particularly in terms of coverage rates. Furthermore, using a bias-corrected linking error is recommended to reduce the influence of sample size on error estimates. Full article

In the era of Industry 4.0, additive manufacturing (AM) technology plays a crucial role in optimizing production processes, especially for small- and medium-sized enterprises (SMEs) striving to enhance competitiveness. Selecting the appropriate material for AM is a complex process that requires considering numerous technical, economic, and environmental criteria. Fuzzy logic-based advisory systems can effectively support decision-making in conditions of uncertainty and subjective user preferences. This study presents a developed advisory system model that uses the Analytic Hierarchy Process (AHP) method and triangular and trapezoidal membership functions, enabling dynamic adjustment of criterion weights. The results demonstrated that the system achieved 85% alignment with user preferences, confirming its effectiveness. Future research may focus on integrating fuzzy logic with machine learning algorithms to further enhance the system’s precision and flexibility. Full article