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Found 6374 publications

Characterization of cerebral blood flow oscillations using different classification methods

Balázs Benyó Péter Somogyi Zoltán Benyó Béla Paláncz

Publication Name: IFAC Proceedings Volumes IFAC Papersonline

Publication Date: 2005-01-01

Volume: 16

Issue: Unknown

Page Range: 214-219

Description:

Oscillation of the cerebral blood flow (CBF) is a common feature in several physiological or pathophysiological states of the brain. It is a promising opportunity to identify the disorders of the cerebral circulation based on the classification of CBF signals. Three classification models are developed with different heuristic in order to carry out the systematic classification based on a problem specific feature extraction. The efficiency of the selected classification methods are evaluated and compared. Copyright © 2005 IFAC.

Open Access: Yes

DOI: 10.3182/20050703-6-cz-1902.02150

Population-Level Assessment of Circumferential Flank Waviness Variability Using a ΔW1 Indicator Derived from CMM Measurements

Krisztián Horváth

Publication Name: Applied Sciences Switzerland

Publication Date: 2026-03-01

Volume: 16

Issue: 6

Page Range: Unknown

Description:

Long-wavelength flank waviness plays a critical role in the excitation behavior of geared transmissions. While coordinate measuring machine (CMM) exports provide detailed geometric information, conventional evaluations typically focus on individual tooth curves and do not quantify circumferential inhomogeneity across teeth. This study introduces a tooth-to-tooth long-wavelength waviness inhomogeneity indicator (ΔW1) derived directly from Klingelnberg-style MKA plot files and demonstrates its behavior on a large industrial dataset comprising 3375 measured gear parts. Each flank curve was detrended using a second-order polynomial fit, and lobe-based waviness amplitudes (W1–W3) were extracted via sine–cosine projection. The proposed ΔW1 metric was defined as the difference between the maximum and minimum W1 values across measured teeth within the same part. To eliminate measurement edge effects, a mid-section evaluation (10–90% of the face width) was additionally performed. Population-level analysis revealed consistent separation between geometrically homogeneous and inhomogeneous parts, with ΔW1 values in the most critical components exceeding 7–9 µm after mid-section filtering. Unsupervised clustering based on ΔW1 and maximum W1 further distinguished a high-variability subset of parts exhibiting systematic long-wavelength modulation patterns. The results demonstrate that circumferential waviness variability can be quantified using standard CMM outputs without additional hardware or specialized measurement procedures. The proposed indicator provides a practical geometric screening tool for large production batches and establishes a reproducible framework for linking detailed flank geometry to manufacturing consistency assessment. Although acoustic validation is outside the scope of the present work, the metric is intended as an NVH-relevant geometric risk indicator for future vibroacoustic correlation studies.

Open Access: Yes

DOI: 10.3390/app16063037

Chitosan and cyanobacterial biomass accounting physiological and biochemical development of winter wheat (Triticum aestivum L.) under nutrient stress conditions

Zoltán Molnár Tibor Janda Lamnganbi Mutum Wogene Kabato

Publication Name: Agrosystems Geosciences and Environment

Publication Date: 2023-12-01

Volume: 6

Issue: 4

Page Range: Unknown

Description:

In the spirit of getting back to nature and using science to increase crop productivity without posing any threat to the environment, researchers are paying attention to making natural products alternative sources of nutrients for plants at affordable prices. On top of this, chitosan and cyanobacteria have become popular in agriculture as metabolic enhancers, biofertilizers, and antimicrobial properties. Cyanobacteria are known to possess biostimulating properties while chitosan is well known for its inherent biological properties. With the aim of minimizing the application of nitrogen, this experiment was conducted for the first time to check if the application of chitosan, microalgae, or both with 50% nitrogen can balance the nutrient requirement for different physiological and biochemical development as effectively as a 100% nitrogen dose. The data were recorded only for the early vegetative stages, as the seeds were non-vernalized. The basic parameters recorded were hexose content, chlorophyll a, chlorophyll b, total phenol content, and relative water content (RWC). In most of the parameters, comparable results were found between the control (with a 100% nitrogen recommended dose) and other treatments (where either microalga, chitosan, or both were added), whereas it was clearly shown that 50% of recommended nitrogen doses reduce the hexose, chlorophyll, and RWCs. Thus, the treatments were effective in supplementing the developmental requirements. Therefore, the combined use of chitosan and cyanobacteria on crops significantly lowers nitrogen fertilization, increases photosynthesis, enhances resistance to water stress, and enhances antioxidant activity in modern agriculture.

Open Access: Yes

DOI: 10.1002/agg2.20428

Harnessing the Synergy of the Cyanobacteria-Plant Growth Promoting Bacteria for Improved Maize (Zea mays) Growth and Soil Health

Zoltán Molnár Tibor Janda Lamnganbi Mutum Wogene Kabato Mariann Rakszegi

Publication Name: Sustainability Switzerland

Publication Date: 2023-12-01

Volume: 15

Issue: 24

Page Range: Unknown

Description:

Intensive use of chemicals in agriculture harms the soil, disrupts the ecological balance, and impacts microorganisms. Biofertilizers are gaining traction due to their eco-friendly and cost-effective benefits. This study evaluates the potential of the cyanobacterium MACC-612 (Nostoc piscinale) and plant growth-promoting bacteria (PGPB) (Azospirillum lipoferum, Pseudomonas fluorescens) in enhancing crop growth, yield, and soil health. A two-year field study was conducted using a factorial approach and a completely randomized block design, comprising four replications. The three levels of the cynobacterium (0, 0.3, or 1 g/L of N. MACC-612) and different bacteria strains were used in the experiments. The results demonstrated substantial enhancements in seed number per ear, kernel weight, and yield when using N. piscinale and PGPB, whether used individually or in combination. The soil pH, humus, (NO3− + NO2−)-nitrogen, and soil microbial biomass showed significant increases across both years. The combining application of the N. piscinale (0.3 g/L) with A. lipoferum increased grain yield by 33.20% in the first year and 31.53% in the second. The humus and (NO3− + NO2−)-nitrogen content significantly rose in treatments involving N. piscinale at 0.3 g/L combined with A. lipoferum at about 20.25% and 59.2%, respectively, in comparison to the untreated control. Hence, the most effective approach was the combined use of N. piscinale and A. lipoferum, which enhanced maize growth and soil fertility.

Open Access: Yes

DOI: 10.3390/su152416660

Construction of fuzzy signature from data: An example of S ARS pre-clinical diagnosis system

Tamás D. Gedeon Ĺaszĺo T. Ḱoczy Kok Wai Wong

Publication Name: IEEE International Conference on Fuzzy Systems

Publication Date: 2004-12-01

Volume: 3

Issue: Unknown

Page Range: 1649-1654

Description:

There are many areas where objects with very complex and sometimes interdependent features are to be classified; similarities and dissimilarities are to be evaluated. This makes a complex decision model difficult to construct effectively. Fuzzy signatures are introduced to handle complex structured data and interdependent feature problems. Fuzzy signatures can also used in cases where data is missing. This paper presents the concept of a fuzzy signature and how its flexibility can be used to quickly construct a medical pre-clinical diagnosis system. A Severe Acute Respiratory Syndrome (SARS) pre-clinical diagnosis system using fuzzy signatures is constructed as an example to show many advantages of the fuzzy signature. With the use of this fuzzy signature structure, complex decision models in the medical field should be able to be constructed more effectively.

Open Access: Yes

DOI: 10.1109/FUZZY.2004.1375428

Constructing and sampling partite, 3-uniform hypergraphs with given degree sequence

András Hubai Tamás Róbert Mezei Ferenc Béres András Benczúr István Miklós

Publication Name: Plos One

Publication Date: 2024-05-01

Volume: 19

Issue: 5 May

Page Range: Unknown

Description:

Partite, 3-uniform hypergraphs are 3-uniform hypergraphs in which each hyperedge contains exactly one point from each of the 3 disjoint vertex classes. We consider the degree sequence problem of partite, 3-uniform hypergraphs, that is, to decide if such a hypergraph with prescribed degree sequences exists. We prove that this decision problem is NP-complete in general, and give a polynomial running time algorithm for third almost-regular degree sequences, that is, when each degree in one of the vertex classes is k or k − 1 for some fixed k, and there is no restriction for the other two vertex classes. We also consider the sampling problem, that is, to uniformly sample partite, 3-uniform hypergraphs with prescribed degree sequences. We propose a Parallel Tempering method, where the hypothetical energy of the hypergraphs measures the deviation from the prescribed degree sequence. The method has been implemented and tested on synthetic and real data. It can also be applied for χ2 testing of contingency tables. We have shown that this hypergraph-based χ2 test is more sensitive than the standard χ2 test. The extra sensitivity is especially advantageous on small data sets, where the proposed Parallel Tempering method shows promising performance.

Open Access: Yes

DOI: 10.1371/journal.pone.0303155

Innovative public-health strategies for neurodegenerative disease: leveraging diversified ultraviolet irradiation as a next-generation therapy

U. Saeed Ilker Ozsahin Z. Z. Piracha C. Shao-Chun M. Khan

Publication Name: Brazilian Journal of Biology

Publication Date: 2025-01-01

Volume: 85

Issue: Unknown

Page Range: Unknown

Description:

Neurodegenerative diseases (NDs) such as Alzheimer’s, Parkinson’s, Huntington’s disease, and amyotrophic lateral sclerosis are escalating worldwide, straining healthcare systems and leaving patients with therapies that are largely palliative. Emerging evidence positions diversified ultraviolet (UV) irradiation as a groundbreaking, non-invasive strategy to counter these disorders. Beyond its traditional use in sterilization, specific UV spectra, UV-B (280–320 nm), UV-C (200–280 nm), and far-UV (207–222 nm), are now recognized for modulating oxidative stress, restoring mitochondrial function, correcting apoptotic dysregulation, and enhancing DNA repair. Innovative approaches such as riboflavin-mediated phototherapy and photobiomodulation (PBM) show the capacity to disaggregate toxic protein aggregates like β-amyloid and α-synuclein, boost antioxidant defenses, stimulate neurotrophic factors, and quell neuroinflammation. Preclinical models and early clinical trials reveal preserved cognition, enhanced neurogenesis, and reduced disease biomarkers, suggesting real translational promise. From a public-health perspective, UV-based interventions offer a cost-effective, scalable option for aging populations and resource-limited settings, especially when integrated with community-level health technologies and remote delivery platforms. Continued investigation of optimal dosing, long-term safety, and mechanistic pathways will be pivotal to unlock the full therapeutic and population-wide impact of this novel modality.

Open Access: Yes

DOI: 10.1590/1519-6984.297765

Modeling of solid oxide fuel cells and optimal parameter extraction at various operating data using an optimization method

Rania M. Ghoniem Amlak Abaza Ahmed Bayoumi Mahana M. Elbana Ragab A. El-Sehiemy

Publication Name: Plos One

Publication Date: 2026-06-01

Volume: 21

Issue: 6 May

Page Range: Unknown

Description:

One promising technology for a clean and effective energy conversion option is the solid oxide fuel cell (SOFC) being developed for a broad, widespread role in mobile equipment power supply, and stationary power generation. In this endeavor, an optimal design model based on extracted unknown parameters of the SOFC stack, a dimensional nonlinear optimization problem, is developed using the Puma optimization algorithm (POA). The idea of predator-prey relationships in the natural world forms the basis of POA. By implementing innovative and powerful techniques at every stage of exploration and exploitation, this algorithm has enhanced its performance against a broad variety of optimization tasks. Additionally, a new class of intelligent mechanisms, which is a type of phase change hyper-heuristic, is proposed. There are four operating circumstances in which the stack model is tested: four temperatures in the range 923–1073 K and 3 bar, with two conditions for validation and the others for testing the model. The proposed POA is compared with several well-known algorithms. The findings of the simulation are contrasted with those from published works using the Marine Predator Algorithm (MPA), Moth Flame Algorithm (MFA), Sine Cosine Algorithm (SCA), and Grey Wolf Optimizer (GWO), demonstrating the superior performance of POA in comparison to these competitive algorithms. Under different operating conditions, the computed polarization curves, V-I and P-I, closely resemble the measured datasets. Statistical indices and the ANOVA test confirm that there are differences in the mean values among the optimizer groups, demonstrating the viability and robustness of the proposed optimizer in comparison to other recent complex optimizers. Finally, the proposed POA yields significantly improved parameters with good convergence rates across various SOFC operating conditions.

Open Access: Yes

DOI: 10.1371/journal.pone.0350332

Enablers of Augmented Reality in the Food Supply Chain: A Systematic Literature Review

John G. Keogh Abderahman Rejeb Karim Rejeb

Publication Name: Journal of Foodservice Business Research

Publication Date: 2021-01-01

Volume: 24

Issue: 4

Page Range: 415-444

Description:

The food industry represents one of the most critically important sectors of the economy. Food is a basic necessity for human life and requires specialized handling, preparation, and logistics to be safe for human consumption. Moreover, food supply chains have become more globalized, fragmented, and complex. Food industry stakeholders are forced to reconsider conventional ways of managing food chains and coping with the latest technology innovation trends. As technology unlocks several opportunities in supply chains, food businesses have a vested interest in exploring Augmented Reality (AR). AR denotes the technology of overlaying virtual objects upon the real-world environment. Despite being a well-established technology, AR possibilities in the food supply chain remain an unexplored research area. Therefore, to fill this knowledge gap and capture the latest developments in this field, we conducted a systematic literature review. Fifty-one (51) publications were thoroughly analyzed to identify the enablers of AR in the food industry. Findings revealed five main areas where AR potentially offers substantial business value. These include food process efficiencies, food decision-making, food marketing, food training, and food safety. Finally, research contributions, trajectories and limitations were highlighted.

Open Access: Yes

DOI: 10.1080/15378020.2020.1859973