Search Results (19)

Trajectory tracking is a crucial task for autonomous mobile robots, requiring smooth and safe execution in dynamic environments. This study uses a nonlinear model predictive controller (MPC) to ensure accurate trajectory tracking of a four-wheel drive, four-wheel steer (4WD-4WS) mobile robot. However, the MPC’s performance depends on the optimal tuning of its key parameters, a challenge addressed using the Multivariate Gaussian Mixture Model Continuous Ant Colony Optimization (MGMM-${\mathrm{ACO}}_R$) algorithm. This method improves on the classic ${\mathrm{ACO}}_R$ algorithm by overcoming two major limitations: the lack of consideration for interdependencies between optimized variables, and an inadequate balance between global exploration and local exploitation. The proposed approach is validated by a two-phase evaluation. Firstly, benchmark function evaluations demonstrate its superiority over established optimization algorithms, including ACO, ${\mathrm{ACO}}_R$, and PSO and its variants, in terms of convergence speed and solution accuracy. Secondly, MGMM-${\mathrm{ACO}}_R$ is integrated into the MPC framework and tested in various scenarios, including trajectory tracking with circular and eight trajectories and dynamic obstacle avoidance during trajectory tracking. The results, evaluated based on trajectory error, control effort, and computational latency, confirm the robustness of the proposed method. In particular, the explicit modeling of correlations between variables in MGMM-${\mathrm{ACO}}_R$ guarantees stable, collision-free trajectory tracking, outperforming conventional ${\mathrm{ACO}}_R$-based approaches that optimize variables independently. Full article

The negative ecological impact of industrialization, which involves the use of petroleum products and dyes in the environment, has prompted research into effective, sustainable, and economically beneficial green technologies. For green remediation primarily based on active microbial metabolites, these microbes are typically from relevant sources. Active microbial metabolite production and genetic systems involved in xenobiotic degradation provide these microbes with the advantage of survival and proliferation in polluted ecological niches. In this study, we evaluated the ability of wheat root-associated L. fusiformis MGMM7 to degrade xenobiotic contaminants such as crude oil, phenol, and azo dyes. We sequenced the whole genome of MGMM7 and provided insights into the genomic structure of related strains isolated from contaminated sources. The results revealed that influenced by its isolation source, L. fusiformis MGMM7 demonstrated remediation and plant growth-promoting abilities in soil polluted with crude oil. Lysinibacillus fusiformis MGMM7 degraded up to 44.55 ± 5.47% crude oil and reduced its toxicity in contaminated soil experiments with garden cress (Lepidium sativum L.). Additionally, L. fusiformis MGMM7 demonstrated a significant ability to degrade Congo Red azo dye (200 mg/L), reducing its concentration by over 60% under both static and shaking cultivation conditions. However, the highest degradation efficiency was observed under shaking conditions. Genomic comparison among L. fusiformis strains revealed almost identical genomic profiles associated with xenobiotic assimilation. Genomic relatedness using Average Nucleotide Identity (ANI) and digital DNA–DNA hybridization (DDH) revealed that MGMM7 is distantly related to TZA38, Cu-15, and HJ.T1. Furthermore, subsystem distribution and pangenome analysis emphasized the distinctive features of MGMM7, including functional genes in its chromosome and plasmid, as well as the presence of unique genes involved in PAH assimilation, such as phnC/T/E, which is involved in phosphonate biodegradation, and nemA, which is involved in benzoate degradation and reductive degradation of N-ethylmaleimide. These findings highlight the potential properties of petroleum-degrading microorganisms isolated from non-contaminated rhizospheres and offer genomic insights into their functional diversity for xenobiotic remediation. Full article

The detection of two-line element (TLE) outliers and space events play a crucial role in enhancing spatial situational awareness. Therefore, this paper addresses the issue of TLE outlier detection methods that often overlook the mutual influence of multiple factors. Hence, a Multivariate Gaussian Mixture Model (MGMM) is introduced to consider the interdependencies among various indicators. Additionally, a Multi-strategy Genetic Algorithm (MGA) is employed to adjust the complexity of the MGMM, allowing it to accurately learn the actual distribution of TLE data. Initially, the proposed method applies probabilistic fits to the predicted error rate changes for both the TLE semi-major axis and the orbital inclination. Chaos initialization, a posterior probability penalty, and local optimization iterations are subsequently integrated into the genetic algorithm. These enhancements aim to estimate the MGMM parameters, addressing issues related to poor robustness and the susceptibility of the MGMM to converge to local optima. The algorithm’s effectiveness is validated using TLE data from typical space targets. The results demonstrate that the optimized algorithm can efficiently detect outliers and maneuver events within complex TLE data. Notably, the comprehensive detection performance index, measured, using the F1 score, improved by 15.9% compared to the Gaussian mixture model. This significant improvement underscores the importance of the proposed method in bolstering the security of complex space environments. Full article

A significant weakness of many organic and inorganic aerogels is their poor mechanical behaviour, representing a great impediment to their application. For example, polymer aerogels generally have higher ductility than silica aerogels, but their elastic modulus is considered too low. Herein, we developed extremely low loading (<1 wt%) 2D graphene oxide (GO) nanosheets modified poly (vinyl alcohol) (PVA) aerogels via a facile and environmentally friendly method. The aerogel shows a 9-fold increase in compressional modulus compared to a pure polymer aerogel. With a low density of 0.04 mg/mm³ and a thermal conductivity of only 0.035 W/m·K, it outperforms many commercial insulators and foams. As compared to a pure PVA polymer aerogel, a 170% increase in storage modulus is obtained by adding only 0.6 wt% GO nanosheets. The nanocomposite aerogel demonstrates strong fire resistance, with a 50% increase in burning time and little smoke discharge. After surface modification with 1H,1H,2H,2H-Perfluorodecyltriethoxysilane, the aerogel demonstrates water resistance, which is suitable for outdoor applications in which it would be exposed to precipitation. Our research demonstrates a new pathway for considerable improvement in the performance and application of polymer aerogels. Full article

The current study sought to investigate the changes in surface hardness, roughness, and moisture absorption of the Vertex ThermoSens polymer (Vertex Dental, 3D Systems, The Netherlands) following immersion in artificial saliva for various periods (7, 14, and 28 days). A total of 60 rectangular specimens with dimensions of 20 mm in length, 20 mm in width, and 3 mm in thickness were made. Due to insufficient mold solidification, these specimens were made utilizing the injection molding process. A Mitutoyo Surftest 4 roughness meter (Mitutoyo, Aurora, IL, USA) was used to measure the surface roughness of the test materials. The ThermoSens polymer hardness was assessed using the Shor method and D—HSD scale, while absorption was measured with a Sartorius analytical balance. Results indicated the highest mean hardness after 28 days (M = 77.6) (Surface 1) and the lowest for the control group (M = 59) (Surface 2). The maximum surface roughness occurred in direction 2.2 pre-immersion (Ra = 2.88 μm) and 7 days post-removal (Ra = 2.95 μm). The control group exhibited the lowest absorption (Wsp = 1.524 mg/mm³), with the highest mean values over 28 days (Wsp = 1.541 mg/mm³). The elevated flask and plaster temperature slowed polymer solidification, resulting in longer macromolecules and improved mechanical properties and surface features. Full article

Plant pathogens present a major challenge to crop production, leading to decreased yield and quality during growth and storage. During long-term storage, healthy onions can develop diseases from latent pathogen infections. This poses a challenge for onion growers because infected bulbs without visible symptoms can lead to significant crop losses during the growing season. In this study, we aimed to isolate and identify Fusarium species from yellow onion bulbs (Allium cepa L.) that developed disease symptoms during storage. The aggressiveness of these strains against onion bulbs and seedlings was also evaluated. The isolated strains were further subjected to morphological and molecular differentiation. The results revealed that all 16 isolated strains belonged to the Fusarium complex species incarnatum-equiseti and Fusarium fujikuroi, namely, F. proliferatum (98%), F. oxysporum (1%), and Fusarium sp. (1%). Koch’s postulate analysis of isolated strains revealed varying aggressiveness on onion bulbs and plants depending on fungal species. Disease symptoms developed more slowly on plants than on onion bulb plants according to Koch’s postulates. Moreover, the results revealed that Fusarium strains that can infect onion plants were less pathogenic to onion bulbs and vice versa. In addition, three isolates were found to be non-pathogenic to onions. Furthermore, the in vitro control of Fusarium species through Bacillus velezensis KS04-AU and Streptomyces albidoflavus MGMM6 showed high potential for controlling the growth of these pathogenic fungi. These results may contribute to the development of environmentally friendly approaches for controlling onion spoilage caused by pathogens during storage. Full article

The purpose of this study was to investigate the most appropriate methodological approach for the automatic measurement of rodent myocardial infarct polar map using histogram-based thresholding and unsupervised deep learning (DL)-based segmentation. A rat myocardial infarction model was induced by ligation of the left coronary artery. Positron emission tomography (PET) was performed 60 min after the administration of ¹⁸F-fluoro-deoxy-glucose (¹⁸F-FDG), and PET was performed after injecting ⁶⁴Cu-pyruvaldehyde-bis(N4-methylthiosemicarbazone). Single photon emission computed tomography was performed 60 min after injection of ^99mTc-hexakis-2-methoxyisobutylisonitrile and ²⁰¹Tl. Delayed contrast-enhanced magnetic resonance imaging was performed after injecting Gd-DTPA-BMA. Three types of thresholding methods (naive thresholding, Otsu’s algorithm, and multi-Gaussian mixture model (MGMM)) were used. DL segmentation methods were based on a convolution neural network and trained with constraints on feature similarity and spatial continuity of the response map extracted from images by the network. The relative infarct sizes measured by histology and estimated R² for ¹⁸F-FDG were 0.8477, 0.7084, 0.8353, and 0.9024 for naïve thresholding, Otsu’s algorithm, MGMM, and DL segmentation, respectively. DL-based method improved the accuracy of MI size assessment. Full article

Anthropogenic pollution, including residues from the green revolution initially aimed at addressing food security and healthcare, has paradoxically exacerbated environmental challenges. The transition towards comprehensive green biotechnology and bioremediation, achieved with lower financial investment, hinges on microbial biotechnology, with the Rhodococcus genus emerging as a promising contender. The significance of fully annotating genome sequences lies in comprehending strain constituents, devising experimental protocols, and strategically deploying these strains to address pertinent issues using pivotal genes. This study revolves around Rhodococcus erythropolis MGMM8, an associate of winter wheat plants in the rhizosphere. Through the annotation of its chromosomal genome and subsequent comparison with other strains, its potential applications were explored. Using the antiSMASH server, 19 gene clusters were predicted, encompassing genes responsible for antibiotics and siderophores. Antibiotic resistance evaluation via the Comprehensive Antibiotic Resistance Database (CARD) identified five genes (vanW, vanY, RbpA, iri, and folC) that were parallel to strain CCM2595. Leveraging the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) for biodegradation, heavy metal resistance, and remediation genes, the presence of chlorimuron-ethyl, formaldehyde, benzene-desulfurization degradation genes, and heavy metal-related genes (ACR3, arsC, corA, DsbA, modA, and recG) in MGMM8 was confirmed. Furthermore, quorum-quenching signal genes, critical for curbing biofilm formation and virulence elicited by quorum-sensing in pathogens, were also discerned within MGMM8’s genome. In light of these predictions, the novel isolate MGMM8 warrants phenotypic assessment to gauge its potential in biocontrol and bioremediation. This evaluation extends to isolating active compounds for potential antimicrobial activities against pathogenic microorganisms. The comprehensive genome annotation process has facilitated the genetic characterization of MGMM8 and has solidified its potential as a biotechnological strain to address global anthropogenic predicaments. Full article

The classification of point clouds is an important research topic due to the increasing speed, accuracy, and detail of their acquisition. Classification using only color is basically absent in the literature; the few available papers provide only algorithms with limited usefulness (transformation of three-dimensional color information to a one-dimensional one, such as intensity or vegetation indices). Here, we proposed two methods for classifying point clouds in RGB space (without using spatial information) and evaluated the classification success since it allows a computationally undemanding classification potentially applicable to a wide range of scenes. The first is based on Gaussian mixture modeling, modified to exploit specific properties of the RGB space (a finite number of integer combinations, with these combinations repeated in the same class) to automatically determine the number of spatial normal distributions needed to describe a class (mGMM). The other method is based on a deep neural network (DNN), for which different configurations (number of hidden layers and number of neurons in the layers) and different numbers of training subsets were tested. Real measured data from three sites with different numbers of classified classes and different “complexity” of classification in terms of color distinctiveness were used for testing. Classification success rates averaged 99.0% (accuracy) and 96.2% (balanced accuracy) for the mGMM method and averaged 97.3% and 96.7% (balanced accuracy) for the DNN method in terms of the best parameter combinations identified. Full article

Microbial biotechnology plays a crucial role in improving industrial processes, particularly in the production of compounds with diverse applications. In this study, we used bioinformatic approaches to analyze the genomic architecture of Streptomyces albidoflavus MGMM6 and identify genes involved in various metabolic pathways that have significant biotechnological potential. Genome mining revealed that MGMM6 consists of a linear chromosome of 6,932,303 bp, with a high G+C content of 73.5%, lacking any plasmid contigs. Among the annotated genes, several are predicted to encode enzymes such as dye peroxidase, aromatic ring-opening dioxygenase, multicopper oxidase, cytochrome P450 monooxygenase, and aromatic ring hydroxylating dioxygenases which are responsible for the biodegradation of numerous endogenous and xenobiotic pollutants. In addition, we identified genes associated with heavy metal resistance, such as arsenic, cadmium, mercury, chromium, tellurium, antimony, and bismuth, suggesting the potential of MGMM6 for environmental remediation purposes. The analysis of secondary metabolites revealed the presence of multiple biosynthesis gene clusters responsible for producing compounds with potent antimicrobial and metal-chelating activities. Furthermore, laboratory tests conducted under controlled conditions demonstrated the effectiveness of MGMM6 in inhibiting phytopathogenic microbes, decolorizing and degrading aromatic triphenylmethane dyes, particularly Blue Brilliant G250, from wastewater by up to 98 ± 0.15%. Overall, the results of our study highlight the promising biotechnological potential of S. albidoflavus MGMM6. Full article

Interpersonal violence is a rising issue in global society and new approaches are being sought to combat the problem. Within this context, expanding forensic techniques to better document violent crime scenes is critical for improving and acquiring legal evidence, such as proving or tracing contact between victims and suspects. This project aims to demonstrate the potential for forensic investigations in the context of interpersonal violence using a field-based reflective spectroscopy approach. For this, a common cosmetic, Wet‘N’Wild “Color Icon” blush in the shade “Pearlescent Pink”, was mineralogically characterized using transmission electron microscopy and powder X-ray diffraction and subsequently investigated via reflective spectroscopy on a variety of common substrates. Differing amounts of the cosmetic product, ranging from 0.001 g to 0.075 g, were applied to a variety of substrates using a simple push method to simulate forcible contact and material transfer. Substrates included a pine wood block; (calcareous) sand from Tulum, Mexico; Ottawa sand; tile; Pergo wood; linoleum; closet material; carpets; and fabrics. The reflective spectra of cosmetic–substrate combinations were measured via an ASD FieldSpec 4 Hi-Res spectroradiometer. The Wet‘N’Wild cosmetic was reliably detected on various substrates relevant to crime scenes. Minor amounts (as low as 0.02 mg/mm²) could be detected, and average limits of detection of 0.03 mg/mm²) were achieved; however, a calcareous sand (Tulum) had a high level of detection (>0.38 mg/mm²), suggesting that further investigation is needed for more complex sand substrates. The use of the ASD spectroradiometer as a forensic tool within the context of crime scene documentation shows promise. Future work should expand the characterization of cosmetic materials across a broad range of substrates and consider systematic studies of specific population groups. Furthermore, combining this approach with hyperspectral imaging at crime scenes is a promising future direction for crime scene documentation. This work therefore demonstrates a novel method for investigating cosmetics within the context of interpersonal violence and provides a foundation for future laboratory and field work using the ASD FieldSpec 4 and hyperspectral imaging systems. Full article

A simple and compact intensity-interrogated terahertz (THz) relative humidity (RH) sensing platform is successfully demonstrated in experiments on the basis of combining a porous polymer sensing membrane and a continuous THz electronic system. The RH-sensing membrane is fabricated by surface modification of a porous polymer substrate with hydrophilic and photosensitive copolymer brushes via a UV-induced graft-polymerization process. The intensity interrogation sensing scheme indicated that the power reduction of the 0.4 THz wave is dependent on the grafting density of the copolymer brushes and proportional to the RH percent levels in the humidity-controlled air-sealed chamber. This finding was verified by the water contact angle measurement. Based on the slope of the proportional relation, the best sensitivity of the hydrophilic surface-modified sensing membrane was demonstrated at 0.0423 mV/% RH at the copolymer brush density of 1.57 mg/mm³ grafted on the single side of the sensing membrane. The sensitivity corresponds to a detection limit of approximately 1% RH. The THz RH sensing membrane was proven to exhibit the advantages of low loss, low cost, flexibility, high sensitivity, high RH resolution, and a wide RH working range of 25–99%. Thus, it is a good candidate for novel applications of wearable electronics, water- or moisture-related industrial and bio-sensing. Full article

Bacillus species have gained much attention based on their phenotypic characteristics and their genetic architecture as biological control agents and plant growth-promotor with bioremediation potential. In this study, we analyzed the whole genome of a novel strain, Bacillus glycinifermentans MGMM1, isolated from the rhizosphere of a weed plant (Senna occidentalis) and assayed its phenotypic characteristics, as well as antifungal and biocontrol ability. The whole genome analysis of MGMM1 identified 4259 putative coding sequences, with an encoding density of 95.75% attributed to biological functions, including genes involved in stimulating plant growth, such as acetolactate synthase, alsS, and genes involved in the resistance to heavy metal antimony (arsB and arsC). AntiSMASH revealed the presence of biosynthetic gene clusters plipastatin, fengycin, laterocidine, geobacillin II, lichenysin, butirosin A and schizokinen. Tests in vitro confirmed that MGMM1 exhibited antifungal activity against Fusarium oxysporum f.sp. radicis-lycopersici (Forl) ZUM2407, Alternaria alternata, F. graminearum and F. spp. and produce protease, lipase amylase and cellulase. Bacillus glycinifermentans MGMM1 demonstrated proteolytic (4.82 ± 1.04 U/mL), amylolytic (0.84 ± 0.05 U/mL) and cellulosic (0.35 ± 0.02 U/mL) enzymatic activities, as well as indole-3-acetic acid production (48.96 ± 1.43 μg/mL). Moreover, the probiotic strain MGMM1 demonstrated a high biocontrol potential of inhibiting (up to 51.45 ± 8.08%) the development of tomato disease caused by Forl ZUM2407. These results suggest that B. glycinifermentans MGMM1 has significant potential as a biocontrol, plant growth-promoting agent in agriculture. Full article

The present work evaluates the thermal behavior of graphene oxide (GO) when deposited on 3D-printed polylactic acid (PLA), in order to develop a medical device for photothermal therapy applications. An experimental–numerical analysis was performed to assess the photothermal conversion capacity, based on the power emitted by a NIR (785 nm) laser, and the subsequent temperature distribution on the GO-PLA material. The influence of the deposited mass of GO and the PLA thickness was studied through 40 different scenarios. The results estimated a value of photothermal conversion efficiency of up to 32.6%, achieved for the lower laser power density that was tested (0.335 mW/mm²), and a high mass value of deposited GO (1.024 × 10⁻³ mg/mm²). In fact, an optimal mass of GO in the range of 1.024–2.048 × 10⁻³ mg/mm² is proposed, in terms of absorption capacity, since a higher mass of GO would not increase the conversion efficiency. Moreover, the study allowed for an estimation of the thermal conductivity of this specific biomaterial (0.064 W/m·K), and proved that a proper combination of GO mass, PLA thickness, and laser power can induce ablative (>60 °C, in a concentrated area), moderate (50 °C), and mild (43 °C) hyperthermia on the bottom face of the biomaterial. Full article

A new mechanism, applied in this study as a biomechanical device, known as a Bioactive Kinetic Screw (BKS) for bone implants is described. The BKS was designed as a bone implant, in which the bone particles, blood, cells, and protein molecules removed during bone drilling are used as a homogeneous autogenous transplant at the same implant site, aiming to optimize the healing process and simplify the surgical procedure. In this work, the amount of bone that will be compacted inside and around the new biomechanism was studied, based on the density of the bone applied. This study allows us to analyze the average bone density in humans (1.85 mg/mm³ or 1850 µg/mm³) with four different synthetic bone densities (Sawbones PCF 10, 20, 30 and 40). The results show that across all four different synthetic bones densities, the bone within the new model is 3.45 times denser. After a pilot drill (with 10 mm length and 1.8 mm diameter), in cases where a guide hole is required, the increase in ratio is equal to 2.7 times inside and around the new biomechanism. The in vitro test validated the mathematical results, describing that in two different materials, the same compact factor of 3.45 was determined with the new biomechanical device. It was possible to describe that BKS can become a powerful tool in the diagnosis and treatment of natural bone conditions and any type of disease. Full article