Original Method Research Articles

Fucoidans from Laminaria hyperborea demonstrate bactericidal activity against diverse bacteria

AbstractFucoidans are a heterogenous class of fucose-rich sulfated carbohydrates which have attracted increasing attention in cancer and inflammation research due to their bioactive properties. There are reports that fucoidans may have direct antibacterial effects and synergy with antibiotics. However, the literature is conflicting, potentially due to the limited reporting of origin, characteristics, and extraction methods of the fucoidans tested. Here we report the results of 18 defined fucoidans screened for direct, indirect, and synergistic antibacterial effects. 15 distinct fucoidan fractions, isolated from Laminaria hyperborea using a solvent-free extraction process, were characterised for molecular weight, pH, viscosity, and sulfur content. These, together with three commercially available crude fractions, were assessed at concentrations from 0.03125-24 mg mL-1 for minimum inhibitory concentration against Staphylococcus aureus, Streptococcus mutans and Streptococcus sanguinis. Furthermore, we tested a selection of fucoidans for antibacterial synergy with vancomycin and indirect antibacterial effects in whole blood survival assays. Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) was performed to assess the stress response in fucoidan-treated S. aureus cultures. We have identified one fucoidan fraction with bactericidal activity against diverse bacteria. This effect is dose-, fucoidan fraction- and bacteria-specific, and furthermore, not related to osmotic stress. No synergistic effects were observed with fucoidan in combination vancomycin. Fucoidans have exciting potential as antimicrobial agents. Further analysis is required to establish the precise molecular characteristics responsible for their potent bactericidal activity.

Disentanglement by Cyclic Reconstruction.

Deep neural networks have demonstrated their ability to automatically extract meaningful features from data. However, in supervised learning, information specific to the dataset used for training, but irrelevant to the task at hand, may remain encoded in the extracted representations. This remaining information introduces a domain-specific bias, weakening the generalization performance. In this work, we propose splitting the information into a task-related representation and its complementary context representation. We propose an original method, combining adversarial feature predictors and cyclic reconstruction, to disentangle these two representations in the single-domain supervised case. We then adapt this method to the unsupervised domain adaptation (UDA) problem, consisting of training a model capable of performing on both a source and a target domain. In particular, our method promotes disentanglement in the target domain, despite the absence of training labels. This enables the isolation of task-specific information from both domains and a projection into a common representation. The task-specific representation allows the efficient transfer of knowledge acquired from the source domain to the target domain. In the single-domain case, we demonstrate the quality of our representations on information retrieval tasks and the generalization benefits induced by sharpened task-specific representations. We then validate the proposed method on several classical domain adaptation (DA) benchmarks and illustrate the benefits of disentanglement for DA.

Hyperthermia as a factor of secondary brain damage in traumatic brain injury

The development of hyperthermia (≥38.3 °C) in severe traumatic brain injury (TBI) is associated with increased levels of disability and mortality, since it critically affects the development of secondary brain damage. At the cellular level, hyperthermia causes structural and functional changes in neurons through disruption of depolarization, transmembrane ion transport, cell signaling mechanisms, and damage to mitochondrial function. Hyperthermia can lead to necrosis or apoptosis of neurons. Among the systemic effects of hyperthermia is an increase in the permeability of the blood - brain barrier, causing the development of intracranial hypertension. This publication presents the pathophysiological mechanisms of the development of hyperthermia and its participation as a factor of brain damage in severe TBI, diagnostic criteria for hyperthermia of central origin and modern methods of controlled normothermia.

Exploring the potentialities of industrial hemp for sustainable rural development

This literature review paper explores the potential sustainable applications of industrial hemp, contextualizing it within the UN Agenda 2030 Sustainable Development Goals (SDGs). Hemp is a dioecious, herbaceous, anemophilous (wind-pollinated) annual crop that grows from one to five meters in height. However, monoecious varieties have been developed through breeding and selection. Historically versatile, Cannabis sativa L. has faced stigmatization and unfounded prohibitions, having played crucial roles in different civilizations, including during World War I. A member of the Cannabaceae family, it is a versatile non-psychoactive crop, with low concentrations of THC. Recently, the global market for hemp has grown. It is an eco-friendly option in the food, textile, pharmaceutical, and energy sectors. This study is based on bibliographic research covering primary and secondary sources, including technical reports, academic journals, and scientific repositories. The origin, characteristics and cultivation methods of industrial hemp are discussed, besides its practical applications, including the production of fibers, health food grains, medicinal phytocannabinoids, biofuels, sustainable biomass energy, and carbon offsetting. Its potential to contribute to sustainable agriculture and achieve SDG targets stands out, considering environmental benefits, product diversification, resistance, and integration into agroforestry systems. Regenerative agriculture is explored as a promising approach, aligned with the principles of sustainable agriculture and the SDGs, aiming to restore agricultural ecosystems, promote soil health, and improve climate resilience. The research highlights the importance of reforming cannabis-related policies to achieve goals relating to health, justice, and global partnerships, emphasizing the need for sustainable approaches to exploiting this natural resource.

Exploring the Influence of Origin, Harvest Time, and Cultivation Method on Antioxidant Capacity and Bioactive Compounds of Matcha Teas.

Matcha, or powdered green tea, has been gaining popularity and is no longer consumed only in the form of infusions, finding new uses in gastronomy and the food industry. The range of teas available on the food market has expanded considerably; hence, the aim of this study was to determine, for the first time, the antioxidant capacity and contents of antioxidant compounds in various Matcha teas available on the Polish market, taking into account the country of origin, time of harvest, and conventional vs. organic cultivation. Eleven green-tea powders were used in the analyses performed using spectrophotometric methods (Trolox equivalent antioxidant capacity, Ferric-Ion-Reducing Antioxidant Power, Total Polyphenol Content, Total Flavonoid Content, Vitamin C Content) and HPLC methods (polyphenolic acids, flavonoids, and caffeine). Antioxidant capacity ranged from 7.26 to 9.54 mM Trolox equivalent/L while reducing power ranged from 1845.45 to 2266.12 Fe(II)/L. Total phenolic content amounted to 820.73-1017.83 mg gallic acid equivalent/L, and total flavonoid content was 864.71-1034.40 mg rutin equivalent /L. A high vitamin C content was found, ranging from 38.92 to 70.15 mg/100 mL. Additionally, a high content of caffeine that ranged between 823.23 and 7313.22 mg/L was noted. Moreover, a high content of polyphenolic compounds, including epicatechin gallate, myricetin, gallic acid, and 4-hydroxybenzoic acid, was found. The phytochemical composition and antioxidant properties depended on the harvest time, type of cultivation, and country of origin. Therefore, Matcha tea infusions have been shown to be a valuable source of antioxidants that can be used in the daily diet.

Intelligent UAV fire monitoring method based on improved YOLOv5 algorithm

Aiming at the existing UAV fire detection system with low small target detection accuracy, a high leakage rate, and a slow rate, an improved YOLOv5 UAV flame detection algorithm is proposed. First, the anchor box clustering is optimized using the K-mean++algorithm to reduce the classification error rate. Second, the original backbone network is enhanced with the CBAM attention mechanism, which scans the whole globe to obtain the target area with a high weighting proportion and needs to be focused on. Replace the PANet network with the BiFPN network in the neck and introduce jump connections when performing feature fusion, which can better retain the semantic information of high-level and low-level features. Finally, the α-IoU loss function is added to achieve the regression accuracy of different levels of the bounding box by modulating α, which improves the detection accuracy of small datasets and the robustness to noise. According to the experimental results, using a randomly segmented dataset, the modified YOLOv5 algorithm obtains a mAP value of 80.2%, which is 6.7% higher than the original YOLOv5 method, while maintaining an FPS of 64 frames per second. The method helps to improve the accuracy of UAVs for fire monitoring, and the performance is better than the existing flame detection algorithms, which meet the requirements of practical applications.

Research Note: A Novel Sullivan Method Projection Framework With Application to Long COVID.

Originally developed for estimating healthy life expectancy, the traditional Sullivan method continues to be a popular tool for obtaining point-in-time estimates of the population impacts of a wide range of health and social conditions. However, except in rare data-intensive cases, the method is subject to stringent stationarity assumptions, which often do not align with real-world conditions and restrict its resulting estimates and applications. In this research note, we present an expansion of the original method to apply within a population projection framework. The Sullivan method projection framework produces estimates that offer new insights about future trends in population health and social arrangements under various demographic and epidemiologic scenarios, such as the percentage of life years that population members can expect to spend with a condition of interest in a time interval under different assumptions. We demonstrate the utility of this framework using the case of long COVID, illustrating both its operation and potential to reveal insights about emergent population health challenges under various theoretically informed scenarios. The traditional Sullivan method provides a summary measure of the present, while its incorporation into a projection framework enables preparation for a variety of potential futures.

Exploring transient global transcriptional changes induced by ascorbic acid revealed via atKAS-seq profiling.

Transcription initiates the formation of single-stranded DNA (ssDNA) regions within the genome, delineating transcription bubbles, a highly dynamic genomic process. Kethoxal-assisted single-stranded DNA sequencing (KAS-seq) utilizing N3-kethoxal has emerged as a potent tool for mapping specific guanine positions in ssDNA on a genome-wide scale. However, the original KAS-seq method required the costly Accel-NGS Methyl-seq DNA library kit. This study introduces an optimized iteration of the KAS-seq technique, referred to as adapter-tagged KAS-seq (atKAS-seq), incorporating an adapter tagging strategy. This modification involves integrating sequencing adapters via complementary strand synthesis using random N9 tagging. Additionally, by harnessing the potential of ascorbic acid (ASC), recognized for inducing global epigenetic changes, we employed the atKAS-seq methodology to elucidate critical pathways influenced by short-term, high-dose ASC treatment. Our findings underscore that atKAS-seq enables rapid and precise analyses of transcription dynamics and enhancer activities concurrently. This method offers a streamlined, cost-efficient, and low-input approach, affirming its utility in probing intricate genomic regulatory mechanisms.

Characterization of the Source of Heroin by Ultra-High-Performance Liquid Chromatography with Tandem Mass Spectrometry (UPLC-MS/MS) and Principal Component Analysis (PCA)

The sources of seized heroin can provide information on its manufacture and distribution. Using ultra-high-performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS), 15 neutral/acidic characteristic target substances in illicit heroin products were determined. The analysis of these samples was completed by combining mathematical and statistical analysis. In all, 38 heroin samples from different cities were characterized by combining case information, relevant subgroups, and principal component analysis (PCA) results. The samples were initially classified using principal component analysis (PCA). The Pearson distance was used to calculate the correlation between samples in each group separately, and r > 0.98 was selected as the threshold value to screen out 11 groups with strong correlations. It was surmised from the case information that the origin or processing methods of heroin seized from different cities may differ. This study applies correlation analysis to relate the seized heroin from different cases to the same upline or network, providing insight for the investigation.

Comprehensive optical and thermal investigation of optimal near-infrared absorption enhancement of nano-patterned aluminum

Compelling electromagnetic (EM) properties, such as tight EM-confinement and fabrication-compatible architectures, propel the importance of metal-based nanophotonics in small-footprint devices, such as highly efficient optical absorbers. Here, we focus on boosting the absorption of optically thick bulk Aluminum (Al), the most abundant metal, solely by corrugating its surface periodically. Application-oriented, we utilize the simplest one-dimensional (1D) rectangular-grating pattern that can be remotely engraved on a large scale, e.g., using femtosecond pulsed lasers. We devise four designs for various utilizations optimized for perfect absorption around the application-abundant 1.06μm wavelength under transverse electric (TE), transverse magnetic (TM), and unpolarized (denoted TEM) irradiations. Lacking attractive plasmonic confinement properties, the s-polarization is considered electromagnetically “degenerate” to the p-polarization case, making it rarely explored in the literature of 1D-grating architectures. Nevertheless, we reveal that perfect s-polarized absorption is also attainable in such designs without adding extra layers. The designs’ performance, properties, limitations, and the propelling electromagnetic physics are theoretically and numerically discussed in-depth. A large-area sample of the TEM-optimized design was fabricated, facilitating uniquely extensive optical and radiative heating experimental investigation with realistic near-to-normal Gaussian-beam incidence. The absorption enhancement derived from wideband and single-frequency optical and radiative heating measurements agree well, also with the numerical simulations. Despite fabrication-derived thickness deviation, the design experimentally showed a 9.6-(TE), 6-(TM), and 7.8-(TEM) fold absorption enhancement over bulk Al. Considering the one-dimensional-patterning simplicity and its large-area fabrication compatibility, it is noteworthy and pioneering, particularly for the challenging s-polarization case. We also study a polarization-sensitive diffused reflection scattering pattern, evidently postulated to result from resonated surface roughness, highlighting the importance of radiative heating measurements. The elaborated original findings and methods are highly applicable alongside the prevalent material and spectrum.

Comprehensive identification of pathogenic microbes and antimicrobial resistance genes in food products using nanopore sequencing-based metagenomics

Foodborne pathogens, particularly antimicrobial-resistant (AMR) bacteria, remain a significant threat to global health. Given the limitations of conventional culture-based approaches, which are limited in scope and time-consuming, metagenomic sequencing of food products emerges as a promising solution. This method provides a fast and comprehensive way to detect the presence of pathogenic microbes and antimicrobial resistance genes (ARGs). Notably, nanopore long-read sequencing provides more accurate bacterial taxonomic classification in comparison to short-read sequencing. Here, we revealed the impact of food types and attributes (origin, retail place, and food processing methods) on microbial communities and the AMR profile using nanopore metagenomic sequencing. We analyzed a total of 260 food products, including raw meat, sashimi, and ready-to-eat (RTE) vegetables. Clostridium botulinum, Acinetobacter baumannii, and Vibrio parahaemolyticus were identified as the top three foodborne pathogens in raw meat and sashimi. Importantly, even with low pathogen abundance, higher percentages of samples containing carbapenem and cephalosporin resistance genes were identified in chicken and RTE vegetables, respectively. In parallel, our results demonstrated that fresh, peeled, and minced foods exhibited higher levels of pathogenic bacteria. In conclusion, this comprehensive study offers invaluable data that can contribute to food safety assessments and serve as a basis for quality indicators.

Assessing the influence of biomass origin and fractionation methods on pyrolysis of primary biomass fractions

Pyrolysis has emerged as a highly promising technology for the efficient utilization of organic materials, particularly lignocellulosic materials. This study investigates the behavior and influence of three primary biomass fractions (cellulose, hemicellulose, and lignin) derived from different sources (trees, shrubs, and agricultural residues) on the kinetic of the pyrolysis process. With this objective, different fragmentation methods were employed to isolate the main components of the lignocellulosic biomass, followed by thermogravimetric analysis to examine the pyrolysis process. The activation energy was determined using both, the Friedman differential kinetic models of and the Flynn-Wall-Ozawa integrated method, with the latter being found to be more efficient. The activation energy (Ea) was found to be influenced by several factors, including the fractionation process and the origin of the raw material. For hemicellulose, the Ea was in the range of 200–300 kJ mol−1, while cellulose showed Ea values of about 180–220 kJ mol−1. In contrast, lignin displayed a wider range of Ea variation, with its values being more sensitive to the fractionation method used. Gas samples were collected through adsorption tubes during pyrolysis for analysis of gas stream compositions from different lignocellulosic fractions using gas chromatography/mass spectrometry. Furfural, among other compounds, was predominantly detected in the pyrolysis of hemicellulose fractions, ranging from 6.6 % to 19.3 %.

Seed Morphological Analysis in Species of Vitis and Relatives

Seed shape descriptions of species of Vitis have traditionally been based on adjectives comparing overall shape with geometric figures, such as oval, elongated oval, and pear-shaped, corresponding to higher values of the Stummer index (lower aspect ratio) for oval, and lower values of the Stummer index for pear shape (or elongated seeds, with a higher aspect ratio). Analytical, quantitative descriptions of shape have recently been applied to diverse genera of Vitaceae and cultivated varieties of Vitis. Here, we present the application of three quantitative methods to the seed shape description of ten species of the genus Vitis and three species of related genera (Ampelopsis, Cissus and Parthenocissus). First, general seed shape was described through comparisons using geometric models. For this, the average silhouettes of representative seed populations were used as models for shape quantification. Two additional quantitative methods were based on the measurement of bilateral symmetry and curvature analysis in the apex. Quantitative methods for shape description based on similarity with the models give an accurate account of the relationships between Vitis species. The resulting dendrogram is like the dendrogram obtained from a combined analysis using the data from general measurements and curvature and symmetry analyses. The original methods presented here for seed morphology are useful for analyzing the phylogenetic relationships between species of Vitis.

Kano Model Categorization Methods: Typology and Systematic Critical Overview for Hospitality and Tourism Academics and Practitioners

The Kano model has been widely applied in multiple disciplines, particularly in product or service development and optimization. Although the original categorization framework remains the most applied, modifications and new approaches have recently been developed. Yet, most of the hospitality and tourism studies continue to use the original Kano method when newer and superior methods are available. The new categorization methods also have flaws, which academics and practitioners should be aware of. The present study uses a systematic literature review approach to provide a comprehensive synthesis and critical analysis of the existing Kano model categorization methods structuring them in groups based on categorization procedures and pointing to the strengths, weaknesses, and fit of each method. The manuscript aims to provide the most current systematic roadmap and “how-to” of the existing methods. It also points to the existing knowledge gaps and directions for future research.

Lignin-rich extracts as slow-release coating for phosphorus fertilizers

Lignin (L) is the most abundant three-dimensional polymer with versatile yet attractive structural features and chemical properties for applicability as a coating. The properties of lignin are mainly related to its botanical origin, structure and extraction method. Although there has been a lot of interest in using lignin in fertilizing systems, only limited attempts have been dedicated to studying the effect of the lignin structure and origin on the release of P fertilizer. Thus, the aim of this work was to study the effect of lignin polymers, extracted from different plant biomass, as coating materials for P fertilizers. Thus, the effect of the structural features originating from the botanical origin on the properties of coated fertilizers and their slow release were established. To this end, three lignocellulosic biomass, namely olive pomace (OP), barley straw (BS) and wood shavings (WS) were used as feedstock to extract lignin polymers. Those chemical compositions and thermochemical properties were characterized prior to their application as coating for triple super phosphate (TSP) fertilizer. Coated fertilizers were characterized for their morphology and coating thickness prior to their evaluation as slow release P-based fertilizer. A distinct slow P release behavior was reported for lignin-coated fertilizers with TSP@L-OP being the slowest as compared to TSP@L-WS, TSP@L-BS as well as pristine TSP, which confirm that the botanical origin and most likely the chemical structures of lignin has significant impact on the properties of the coating materiel. A 100 % release of P from the uncoated TSP was observed after 3 days, while the released amount ranged from only 16.9 % for TSP@L-OP up to 33.4 % for TSP@L-BS. The impact of these coated fertilizers on nutrients uptake and wheat growth was also scrutinized and an noticeable improvement of wheat shoot biomass was observed with the coated TSP treatments reaching up 57.8 % for TSP@BS, 78.1 % for TSP @WS and 93.7 % for TSP @OP, compared to uncoated TSP. This was concomitant to a slight increase of P concentration in shoots, roots and rhizosphere soil, which confirmed the positive effect of slow release P fertilizers on plant growth.

The 30/100 Classification System as a Reference for Price of Sandalwood (Santalum album) Wood in Sandalwood Oil Industry

The perfume and cosmetics industry heavily relies on quality raw materials, particularly Sandalwood oil, known for its unique scent and importance in various products. The presence of a reliable Sandalwood oil distillation industry is crucial for meeting industry demands. However, determining the price of Sandalwood wood faces challenges due to quality variations influenced by factors like geographical origin and processing methods. We have proposed a 30/100 classification system developed by CV. Suraya Nirmala and UPT Laboratorium Terpadu of Universitas Nusa Cendana. The aim of this research is to determine the sandalwood oil content of several raw materials with a 30/100 classification system as a basis for determining the price of these raw materials. The classification method involves categorizing sandalwood into four classes (30, 60, 90, and 100) based on visual observations of the brown heartwood area on the cross-section of the wood sample. The comparative analysis of Sandalwood oil volume indicates variations in oil content among the different classes of Sandalwood wood samples. Sandalwood wood class 90 exhibits a 57.49% Sandalwood oil content compared to class 100. Sandalwood wood class 60 demonstrates a 33.44% Sandalwood oil content compared to class 100. Sandalwood wood class 30 showcases a 19.86% Sandalwood oil content compared to class 100. These findings serve as a reference for determining the pricing of Sandalwood wood as raw material in the Sandalwood oil distillation industry. The price of class 100 serves as the Highest Base Price (Hd), with class 90 priced at 0.5749Hd, class 60 at 0.3344Hd, and class 30 at 0.1986Hd.

Theoretical Study Regarding the General Stability of Upper Chords of Truss Bridges as Beams on Continuous or Discrete Elastic Supports

New or in-service truss bridges, with or without upper bracing systems, may display instability phenomena such as general lateral torsional buckling of the upper chord. The buckling of structural elements, particularly in the case of steel bridges, can be associated with the risk of collapse or temporary/permanent withdrawal from service. Such incidents have occurred in the case of several bridges in different countries: the collapse of the Dee bridge with truss girders in 1847 in Cheshire, England; the collapse of the semi-parabolic truss girder bridge near Ljubičevo over the Morava River in Serbia in 1892; the collapse of the Dysart bridge in Cambria County, Pennsylvania in 2007; the collapse of the Chauras bridge in Uttarakhand, India in 2012; and the collapse of a bridge in Nova Scotia, Canada (2020), and such examples may continue. Buckling poses a significant danger as it often occurs at lower load values compared to those considered during the design phase. Additionally, this phenomenon can manifest suddenly, without prior warning, rendering intervention for its prevention impossible or futile. In contemporary times, most research and design calculation software offer the capability to establish preliminary values for buckling loads, even for highly intricate structures. This is typically achieved through linear eigenvalue buckling analyses, often followed by significantly more complex large displacement nonlinear analyses. However, interpreting the results for complex bridge structures can be challenging, and their accuracy is difficult to ascertain. Consequently, this paper aims to introduce an original method for a more straightforward estimation of the buckling load of the upper chord in steel truss bridges. This method utilizes the theory of beams on discrete elastic supports. The buckling load of the upper chord was determined using both the finite element method and the proposed methodology, yielding highly consistent results.

Research on Voltage Sag Loss Assessment Based on a Two-Stage Taguchi Quality Perspective Method

Voltage sags resulting from symmetrical or asymmetrical faults pose a significant threat to power quality. In response to this challenge, a voltage sag loss assessment method based on a two-stage Taguchi quality perspective approach is proposed to address the quantitative analysis of voltage sag economic losses. Initially, using the Taguchi quality perspective method, single-index quality loss functions are separately established for voltage sag magnitude and fault duration. Subsequently, by introducing a comprehensive load tolerance curve, sensitivity parameters within the quality loss function are accurately calculated. This yields a deterministic model for voltage sag assessment. Building upon this, the relative impact of the two indices on voltage sag loss is evaluated using the quality loss function. Consequently, a comprehensive loss model under the influence of multiple indices is formed by integrating two single-index evaluation models. The simulation results indicate that this method can effectively assess the economic losses of voltage sags under the combined influence of multiple factors. Compared to the original economic loss assessment method, it improves quantitative accuracy by approximately 3.72%. Moreover, the method reduces the computational complexity of loss assessment through the consolidation of intervals with similar sensitivity parameters.

A Rapid RI-TP Model for Predicting Turbine Wake Interaction Broadband Noise

Future UHBR (Ultra-High Bypass-Ratio) engines might cause serious ‘turbine noise storms’ but, at present, turbine noise prediction capability is lacking. The large turning angle of the turbine blade is the first major factor deserving special attention. The RANS (Reynold Averaged Navier–Stokes equation)-informed (here called RI) method and LINSUB (the bound vorticity 2D model LINearized SUBsonic flow in cascade), developed to predict fan broadband noise, coupled with a two-flat-plates (here called TP) assumption for the turbine blade, is applied here, and one autonomous rapid RI-TP model for predicting turbine wake interaction broadband noise has been developed. Firstly, taking the single axial turbine test rig NPU-Turb as the object, both the experimental data and the DDES/AA (delayed Detached Eddy Simulation/Acoustic Analogy) hybrid model have been used to validate the RI-TP model. High consistency in the medium and high frequencies among the three designed and off-designed rotation speeds indicates that the RI-TP model has the ability to predict turbine broadband noise rapidly. And compared with the original RANS-informed method, with one thin-flat-plate assumption on the blade, the RI-TP model can enhance the PWL (sound power level) in almost the whole spectral range below 10 KHz, which, in turn, is closer to the experimental data and the DDES/AA prediction results. The PWL trend with a ‘dividing point’ position is also studied. The spectrum would move up or down if the location is away from true value. In addition, the extraction location for turbulence as an input for the RI-TP model is negligible. In the future, multi-stage characteristics and the blade thickness effect should be further considered when predicting turbine noise.

Iterative optimisation in dynamic environments via local pursuit

We discuss an iterative optimal control approach in which a multi-agent group of control systems discovers locally optimal trajectories in environments with time-varying terrain geometry and moving, deforming obstacles. Works in literature have addressed only special cases of this problem with time-invariant terrain geometry. One such bio-inspired optimal control approach, applicable in static environments, is ‘local pursuit’, a method that iteratively solves partially-constrained final state problems by having agents follow one another, each agent moving optimally towards its predecessor. We show that if, instead, agents evolve in their own time-delayed ‘copy’ of the environment and act based on the most recent time history of their predecessors local neighbourhoods, then the original method can be extended to the dynamic environments described above. Furthermore, we discuss the computational complexity of our approach and demonstrate its effectiveness and computational advantages in a series of simulations.