Search Results (28)

To overcome the negative effects of the biochemical application of nano-substances in medicine (toxicity problem), using the example of fullerene C₆₀’s first derivative (fullerenol, FD-C₆₀), we show that their biophysical effect is possible through non-covalent hydrogen bonds when around FD-C₆₀ water layers are formed. SD-C₆₀ (Zeta potential is −43.29 mV) is much more stable than fullerol (Zeta potential is −25.85 mV), so agglomeration/fragmentation of the fullerol structure, due to instability, can cause toxic effects. When fullerol in solution was exposed to an oscillatory magnetic field with Re (real) part [250/−92 mT, H(ωt) = Acos(ωt)], water layers around FD-C₆₀ (fullerenol) are formed according to the Penrose process of 3D tiling formation, and the second derivative, SD-C₆₀ (or 3HFWC), is self-organized. However, when Im (imaginary) part [250/−92 mT, H(ωt) = Bisin (ωt)] of the external magnetic field is applied in addition to SD-C₆₀, ordered water chains and bubbling of water (“micelle”) are formed as a third derivative (TD-C₆₀). Fullerol (FD-C₆₀) interacts with biological structures biochemically, while the second (SD-C₆₀) and third (TD-C₆₀) derivatives act biophysically via non-covalent hydrogen bond oscillation. SD-C₆₀ and TD-C₆₀ significantly increased water solubility and reduced toxicity. The paper explains the synthesis of SD-C60 and TD-C60 from FD-C60 (fullerol) as a precursor by the influence of an oscillatory magnetic field (“Yin-Yang” principle) on hydrogen bonds in order to create water layers around fullerol. Examples of biomedical applications (cancer and Alzheimer’s) of this synergetic complex are given. This study shows that the “Yin-Yang” machinery, based on the nanophysics of C₆₀ molecules and non-covalent hydrogen bonds, is possible. The first attempt has been composed to synthesize nanomaterial for biophysical vibrational nanomedicine. Full article

Traffic congestion imposes burdens on society and individuals. In 2022, the average congestion cost per auto commuter in the USA was USD1259. New possibilities to increase traffic efficiency are now available as vehicles can interact using Vehicular Ad Hoc Network (VANET) systems, a subset of the Internet of Vehicles (IoV). The traffic assignment problem deals with road network traffic optimization. It is a complex and challenging problem. A few solutions incorporating VANET technology have been presented; most are centralized or depend on infrastructure. In previous work, we introduced Road-ACO, an ant colony optimization (ACO), single-hop, decentralized, infrastructure-less, VANET solution. In this paper, we propose a new multi-hop-enabled, decentralized, ant-colony-inspired algorithm for dynamic highway traffic assignment. The algorithm works for large road networks and requires no infrastructure. We develop Veins framework-based simulations to evaluate the algorithm’s performance. The results indicate that the proposed algorithm consistently outperforms Road-ACO and performs optimally on road segments up to 4000 m long, with improvements of up to 40% on average travel time. Full article

Rising CO₂ levels, as predicted by global climate models, are altering environmental factors such as the water cycle, leading to soil waterlogging and reduced oxygen availability for plant roots. These conditions result in decreased energy production, increased fermentative metabolism, impaired nutrient uptake, reduced nitrogen fixation, and altered leaf gas exchanges, ultimately reducing crop productivity. Co-inoculation techniques involving multiple plant growth-promoting bacteria or arbuscular mycorrhizal fungi have shown promise in enhancing plant resilience to stress by improving nutrient uptake, biomass production, and nitrogen fixation. This study aimed to investigate carbon and nitrogen metabolism adaptations in soybean plants co-inoculated with Bradyrhizobium elkanii, Azospirillum brasilense, and Rhizophagus intraradices under waterlogged conditions in CO₂-enriched environments. Plants were grown in pots in open-top chambers at ambient CO₂ concentration (a[CO₂]) and elevated CO₂ concentration (e[CO₂]). After reaching the V5 growth stage, the plants were subjected to waterlogging for seven days, followed by a four-day reoxygenation period. The results showed that plants’ co-inoculation under e[CO₂] mitigated the adverse effects of waterlogging. Notably, plants inoculated solely with B. elkanii under e[CO₂] displayed results similar to co-inoculated plants under a[CO₂], suggesting that co-inoculation effectively mitigates the waterlogging stress, with plant physiological traits comparable to those observed under elevated CO₂ conditions. Full article

Icing on transmission lines may cause damage to tower components and even lead to structural failure. Aiming at the lack of research on predicting mechanical characteristic parameters of weak components of transmission towers, and the cumbersome steps of building a finite element model (FEM), the study of prediction for mechanical characteristic parameters of weak components of towers based on a finite element simulation and machine learning is proposed. Firstly, a 110 kV transmission tower in a heavily iced area is taken as an example to establish its FEM. The locations of the weak components are analyzed, and the accuracy of FEM is verified. Secondly, meteorological and terrain parameters are considered as input parameters of the prediction model. The axial stresses and nodal displacements of four weak components are selected as output parameters. The FEM of the 110 kV transmission tower is used to obtain input and output datasets. Thirdly, five machine learning algorithms are considered to establish the prediction models for mechanical characteristic parameters of weak components, and the optimal prediction model is obtained. Finally, the accuracy of the prediction method is verified through an actual tower collapse case. The results show that ACO-BPNN is the optimal model that can accurately and quickly predict the mechanical characteristic parameters of the weak components of the transmission tower. This study can provide an early warning for the failure prediction of transmission towers in heavily iced areas, thus providing an important reference for their safe operation and maintenance. Full article

Accurate global monitoring of carbon dioxide (CO₂) is essential for understanding climate change and informing policy decisions. This study compares column-averaged dry-air mole fractions of CO₂ (XCO₂) between ACOS_L2_Lite_FP V9r for Japan’s Greenhouse Gases Observing Satellite (GOSAT), OCO-2_L2_Lite_FP V10r for the USA’s Orbiting Carbon Observatory-2 (OCO-2), and IAPCAS V2.0 for China’s Carbon Dioxide Observation Satellite (TANSAT) collectively referred to as GOT, with data from the Total Carbon Column Observing Network (TCCON). Our findings are as follows: (1) Significant data quantity differences exist between OCO-2 and the other satellites, with OCO-2 boasting a data volume 100 times greater. GOT shows the highest data volume between 30–45°N and 20–30°S, but data availability is notably lower near the equator. (2) XCO₂ from GOT exhibits similar seasonal variations, with lower concentrations during June, July, and August (JJA) (402.72–403.74 ppm) and higher concentrations during December, January, and February (DJF) (405.74–407.14 ppm). XCO₂ levels are higher in the Northern Hemisphere during March, April, and May (MAM) and DJF, while slightly lower during JJA and September, October, and November (SON). (3) The differences in XCO₂ (ΔXCO₂) reveal that ΔXCO₂ between OCO-2 and TANSAT are minor (−0.47 ± 0.28 ppm), whereas the most significant difference is observed between GOSAT and TANSAT (−1.13 ± 0.15 ppm). Minimal differences are seen in SON (with the biggest difference between GOSAT and TANSAT: −0.84 ± 0.12 ppm), while notable differences occur in DJF (with the biggest difference between GOSAT and TANSAT: −1.43 ± 0.17 ppm). Regarding latitudinal variations, distinctions between OCO-2 and TANSAT are most pronounced in JJA and SON. (4) Compared to TCCON, XCO₂ from GOT exhibits relatively high determination coefficients (R² > 0.8), with GOSAT having the highest root mean square error (RMSE = 1.226 ppm, <1.5 ppm), indicating a strong relationship between ground-based observed and retrieved values. This research contributes significantly to our understanding of the spatial characteristics of global XCO₂. Furthermore, it offers insights that can inform the analysis of differences in the inversion of carbon sources and sinks within assimilation systems when incorporating XCO₂ data from satellite observations. Full article

Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however, improvements in detection accuracy are required. Particularly, automated techniques using deep learning on OCT images are being developed to detect various retinal disorders at an early stage. Here, we propose a deep learning-based automatic method for detecting and classifying retinal diseases using OCT images. The diseases include age-related macular degeneration, branch retinal vein occlusion, central retinal vein occlusion, central serous chorioretinopathy, and diabetic macular edema. The proposed method comprises four main steps: three pretrained models, DenseNet-201, InceptionV3, and ResNet-50, are first modified according to the nature of the dataset, after which the features are extracted via transfer learning. The extracted features are improved, and the best features are selected using ant colony optimization. Finally, the best features are passed to the k-nearest neighbors and support vector machine algorithms for final classification. The proposed method, evaluated using OCT retinal images collected from Soonchunhyang University Bucheon Hospital, demonstrates an accuracy of 99.1% with the incorporation of ACO. Without ACO, the accuracy achieved is 97.4%. Furthermore, the proposed method exhibits state-of-the-art performance and outperforms existing techniques in terms of accuracy. Full article

Priming plants with beneficial microbes can establish rapid and robust resistance against numerous pathogens. Here, compelling evidence is provided that the treatment of rapeseed plants with Trichoderma harzianum OMG16 and Bacillus velezensis FZB42 induces defence activation against Verticillium longisporum infection. The relative expressions of the JA biosynthesis genes LOX2 and OPR3, the ET biosynthesis genes ACS2 and ACO4 and the SA biosynthesis and signalling genes ICS1 and PR1 were analysed separately in leaf, stem and root tissues using qRT-PCR. To successfully colonize rapeseed roots, the V. longisporum strain 43 pathogen suppressed the biosynthesis of JA, ET and SA hormones in non-primed plants. Priming led to fast and strong systemic responses of JA, ET and SA biosynthesis and signalling gene expression in each leaf, stem and root tissue. Moreover, the quantification of plant hormones via UHPLC-MS analysis revealed a 1.7- and 2.6-fold increase in endogenous JA and SA in shoots of primed plants, respectively. In roots, endogenous JA and SA levels increased up to 3.9- and 2.3-fold in Vl43-infected primed plants compared to non-primed plants, respectively. Taken together, these data indicate that microbial priming stimulates rapeseed defence responses against Verticillium infection and presumably transduces defence signals from the root to the upper parts of the plant via phytohormone signalling. Full article

Finding reliable and efficient routes is a persistent problem in megacities. To address this problem, several algorithms have been proposed. However, there are still areas of research that require attention. Many traffic-related problems can be resolved with the help of smart cities that incorporate the Internet of Vehicles (IoV). On the other hand, due to rapid increases in the population and automobiles, traffic congestion has become a serious concern. This paper presents a heterogeneous algorithm called ant-colony optimization with pheromone termite (ACO-PT), which combines two state-of-the-art algorithms, pheromone termite (PT) and ant-colony optimization (ACO), to address efficient routing to improve energy efficiency, increase throughput, and shorten end-to-end latency. The ACO-PT algorithm seeks to provide an effective shortest path from a source to a destination for drivers in urban areas. Vehicle congestion is a severe issue in urban areas. To address this issue, a congestion-avoidance module is added to handle potential overcrowding. Automatic vehicle detection has also been a challenging issue in vehicle management. To address this issue, an automatic-vehicle-detection (AVD) module is employed with ACO-PT. The effectiveness of the proposed ACO-PT algorithm is demonstrated experimentally using network simulator-3 (NS-3) and Simulation of Urban Mobility (SUMO). Our proposed algorithm is compared with three cutting-edge algorithms. The results demonstrate that the proposed ACO-PT algorithm is superior to earlier algorithms in terms of energy usage, end-to-end delay, and throughput. Full article

Avian coronaviruses (ACoV) have been shown to be highly prevalent in wild bird populations. More work on avian coronavirus detection and diversity estimation is needed for the breeding territories of migrating birds, where the high diversity and high prevalence of Orthomyxoviridae and Paramyxoviridae have already been shown in wild birds. In order to detect ACoV RNA, we conducted PCR diagnostics of cloacal swab samples from birds, which we monitored during avian influenza A virus surveillance activities. Samples from two distant Asian regions of Russia (Sakhalin region and Novosibirsk region) were tested. Amplified fragments of the RNA-dependent RNA-polymerase (RdRp) of positive samples were partially sequenced to determine the species of Coronaviridae represented. The study revealed a high presence of ACoV among wild birds in Russia. Moreover, there was a high presence of birds co-infected with avian coronavirus, avian influenza virus, and avian paramyxovirus. We found one case of triple co-infection in a Northern Pintail (Anas acuta). Phylogenetic analysis revealed the circulation of a Gammacoronavirus species. A Deltacoronavirus species was not detected, which supports the data regarding the low prevalence of deltacoronaviruses among surveyed bird species. Full article

In this paper, a hybrid optimization controller that combines a genetic algorithm (GA) and ant colony optimization (ACO) called GA-ACO algorithm is proposed. It is applied to a photovoltaic module array (PVMA) to carry out maximum power point tracking (MPPT). This way, under the condition that the PVMA is partially shaded and that multiple peaks are produced in the power-voltage (P-V) characteristic curve, the system can still operate at the global maximum power point (GMPP). This solves the problem seen in general traditional MPPT controllers where the PVMA works at the local maximum power point (LMPP). The improved MPPT controller that combines GA and ACO uses the slope of the P-V characteristic curve at the PVMA work point to dynamically adjust the iteration parameters of ACO. The simulation results prove that the improved GA-ACO MPPT controller is able to quickly track GMPP when the output P-V characteristic curve of PVMA shows the phenomenon of multiple peaks. Comparing the time required for tracking to MPP with different MPPT approaches for the PVMA under five different shading levels, it was observed that the improved GA-ACO algorithm requires 19.5~35.9% (average 29.2%) fewer iterations to complete tracking than the mentioned GA-ACO algorithm. Compared with the ACO algorithm, it requires 74.9~79.7% (average 78.2%) fewer iterations, and 75.0~92.5% (average 81.0%) fewer than the conventional P&O method. Therefore, it is proved that by selecting properly adjusted values of the Pheromone evaporation rate and the Gaussian standard deviation of the proposed GA-ACO algorithm based on the slope scope of the P-V characteristic curves, a better response performance of MPPT is obtained. Full article

Elevated atmospheric CO₂ concentrations (eCO₂) has become the main feature and cause of global change that could affect crop growth in many aspects, including physiological processes and morphological development in plants and nutrient cycling and nutrient uptake from the soil. Studying the responses of crop growth to different nitrogen (N) supply forms under elevated atmospheric CO₂ concentrations can guide nutrient management strategies for agricultural production under future climate change scenarios. Few studies addressed the effect of eCO₂ on N uptake and morphological development for plants. This study was conducted in the CO₂-controlled light incubators based on the sand-pot incubation using wheat and maize as experimental plants. Six treatments were set with two different environmental CO₂ concentrations (aCO₂, 390 µmol mol⁻¹; eCO₂, 690 µmol mol⁻¹) and three different N supply forms, including ammonium−N, nitrate−N and ammonium-nitrate with 1:1 ratio. The following results were obtained: (i) Wheat and maize seedlings, as nitrate-preferring crops, grew better under mixed N forms than under single N forms. For the single N supply treatment, seedlings with nitrate−N supply showed better growth than with ammonium. (ii) For wheat plants, seedlings with a single ammonium−N supply showed slender height and fewer tillers; seedlings with a single nitrate−N supply were characterized by slightly shorter plant height, more tillers, and higher aboveground biomass. (iii) Compared to the aCO₂ group, wheat seedlings with the ammonium−N supply showed an increased maximum root length and a decreased carbon concentration in root exudates; wheat seedlings with a single nitrate−N supply under eCO₂ showed a significant increase in biomass and a decreased carbon concentration in root exudates; wheat seedlings with a mixed N supply under eCO₂ explored a significant increase in carbon concentration in root exudate and a relatively lower N concentration. (iv) For maize plants, seedlings with either single ammonium−N or nitrate−N supply did not show significant differences in most growing indices. Maize seedlings with a mixed N supply exhibited an increase in aboveground biomass and N concentration in root exudates compared to those with a single N supply. (v) Compared with the aCO₂ group, maize seedlings with mixed N supply under eCO₂ conditions exhibited significant increases in plant height, aboveground biomass, and N concentration in root exudates. Single ammonium−N supply was toxic to wheat and maize plants under eCO₂ conditions. We recommend raising the ratio of ammonium-to-nitrate under mixed N supply to improve the coordination of carbon and N metabolism for efficient crop growth under climatic change conditions of elevated atmospheric CO₂ concentration. Full article

Iron (Fe) is an important metal element for the growth of bacteria. Many bacteria respond to Fe limitation through a variety of strategies. We previously isolated an endophyte Bacillus sp. WR13 from wheat root. However, whether and how this strain can cope with Fe-deficient environments remains unclear. In this study, the growth of WR13 under Fe starvation was investigated, and the underlying mechanisms of WR13 in response to Fe starvation were elucidated via genomics and iTRAQ-based proteomics. Under Fe limitation, WR13 showed a growth pattern similar to that of Fe sufficiency. Genomics analysis demonstrated that WR13 had gene clusters related to siderophore synthesis (dhbACEBF), transportation (bcbE), uptake (feuABC-yusV) and hydrolysis (besA). These genes were significantly up-regulated in Fe-starved WR13, which resulted in more siderophore production. Proteomics data revealed that many Fe-containing proteins such as ACO, HemQ, ferredoxin, CNP, and SufD were significantly reduced under Fe limitation. Meanwhile, significant decreases in many proteins involved in glycolysis, TCA cycle, pentose phosphate pathway; asparagine, glutamine, methionine, and serine metabolism; and phospholipid hydrolysis were also observed. Overall, this study shows that Bacillus sp. WR13 was able to respond to Fe limitation via multiple strategies and provides a theoretical basis for the application of WR13 in Fe-deficient soil. Full article

Improving models for managing the networks of firefighting unmanned ground vehicles in crowded areas, as a recommendation system (RS), represented a difficult challenge. This challenge comes from the peculiarities of these types of networks. These networks are distinguished by the network coverage area size, frequent network connection failures, and quick network structure changes. The research aims to improve the communication network of self-driving firefighting unmanned ground vehicles by determining the best routing track to the desired fire area. The suggested new model intends to improve the RS regarding the optimum tracking route for firefighting unmanned ground vehicles by employing the ant colony optimization technique. This optimization method represents one of the swarm theories utilized in vehicles ad–hoc networks and social networks. According to the results, the proposed model can enhance the navigation of self-driving firefighting unmanned ground vehicles towards the fire region, allowing firefighting unmanned ground vehicles to take the shortest routes possible, while avoiding closed roads and traffic accidents. This study aids in the control and management of ad–hoc vehicle networks, vehicles of everything, and the internet of things. Full article

Feature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been proposed to select effective features. However, they achieve differently on medical data, and most of them cannot find those effective features that may fulfill the required accuracy in diagnosing important diseases such as Diabetes, Heart problems, Hepatitis, and Coronavirus, which are targeted datasets in this study. To tackle this drawback, an algorithm is needed that can strike a balance between local and global search strategies in selecting effective features from medical datasets. In this paper, a new binary optimizer algorithm named BSMO is proposed. It is based on the newly proposed starling murmuration optimizer (SMO) that has a high ability to solve different complex and engineering problems, and it is expected that BSMO can also effectively find an optimal subset of features. Two distinct approaches are utilized by the BSMO algorithm when searching medical datasets to find effective features. Each dimension in a continuous solution generated by SMO is simply mapped to 0 or 1 using a variable threshold in the second approach, whereas in the first, binary versions of BSMO are developed using several S-shaped and V-shaped transfer functions. The performance of the proposed BSMO was evaluated using four targeted medical datasets, and results were compared with well-known binary metaheuristic algorithms in terms of different metrics, including fitness, accuracy, sensitivity, specificity, precision, and error. Finally, the superiority of the proposed BSMO algorithm was statistically analyzed using Friedman non-parametric test. The statistical and experimental tests proved that the proposed BSMO attains better performance in comparison to the competitive algorithms such as ACO, BBA, bGWO, and BWOA for selecting effective features from the medical datasets targeted in this study. Full article

Prion gene (PRNP) mutations are associated with diverse disease phenotypes, including familiar Creutzfeldt–Jakob Disease (CJD), Gerstmann–Sträussler–Scheinker disease (GSS), and fatal familial insomnia (FFI). Interestingly, PRNP mutations have been reported in patients diagnosed with Alzheimer’s disease, dementia with Lewy bodies, Parkinson’s disease, and frontotemporal dementia. In this review, we describe prion mutations in Asian countries, including Republic of Republic of Korea, China, and Japan. Clinical phenotypes and imaging data related to these mutations have also been introduced in detail. Several prion mutations are specific to Asians and have rarely been reported in countries outside Asia. For example, PRNP V180I and M232R, which are rare in other countries, are frequently detected in Republic of Korea and Japan. PRNP T188K is common in China, and E200K is significantly more common among Libyan Jews in Israel. The A117V mutation has not been detected in any Asian population, although it is commonly reported among European GSS patients. In addition, V210I or octapeptide insertion is common among European CJD patients, but relatively rare among Asian patients. The reason for these differences may be geographical or ethical isolation. In terms of clinical phenotypes, V180I, P102L, and E200K present diverse clinical symptoms with disease duration, which could be due to other genetic and environmental influences. For example, rs189305274 in the ACO1 gene may be associated with neuroprotective effects in cases of V180I mutation, leading to longer disease survival. Additional neuroprotective variants may be possible in cases featuring the E200K mutation, such as KLKB1, KARS, NRXN2, LAMA3, or CYP4X1. E219K has been suggested to modify the disease course in cases featuring the P102L mutation, as it may result in the absence of prion protein-positive plaques in tissue stained with Congo red. However, these studies analyzed only a few patients and may be too preliminary. The findings need to be verified in studies with larger sample sizes or in other populations. It would be interesting to probe additional genetic factors that cause disease progression or act as neuroprotective factors. Further studies are needed on genetic modifiers working with prions and alterations from mutations. Full article