Maximum Similarity Research Articles

Visual Guidance for User Placement in Avatar-Mediated Telepresence Between Dissimilar Spaces.

Rapid advances in technology gradually realize immersive mixed-reality (MR) telepresence between distant spaces. This paper presents a novel visual guidance system for avatar-mediated telepresence, directing users to optimal placements that facilitate the clear transfer of gaze and pointing contexts through remote avatars in dissimilar spaces, where the spatial relationship between the remote avatar and the interaction targets may differ from that of the local user. Representing the spatial relationship between the user/avatar and interaction targets with angle-based interaction features, we assign recommendation scores of sampled local placements as their maximum feature similarity with remote placements. These scores are visualized as color-coded 2D sectors to inform the users of better placements for interaction with selected targets. In addition, virtual objects of the remote space are overlapped with the local space for the user to better understand the recommendations. We examine whether the proposed score measure agrees with the actual user perception of the partner's interaction context and find a score threshold for recommendation through user experiments in virtual reality (VR). A subsequent user study in VR investigates the effectiveness and perceptual overload of different combinations of visualizations. Finally, we conduct a user study in an MR telepresence scenario to evaluate the effectiveness of our method in real-world applications.

Bioremediation of Cr (VI) by novel microalgal strain

The present study aims at the isolation of Cr (VI) tolerant microalgae from wastewater collected from local bike garages. The wastewater had a continued spillage of engine oil, petrol, grease, and chain lube waste. The isolated microalgae were first sequenced by using 18S rRNA sequencing method. The nucleotide sequence obtained was analyzed via BLAST, the isolated microalga showed maximum similarity with Scenedesmus species. The sequence was submitted to NCBI, and the microalgae was identified as Scenedesmus species GTAF1 IU accession no. OM903800. The bioremediation potential of the isolated microalgae was evaluated for the removal of Cr (VI) in synthetic wastewater. The Cr (VI) removal efficiency was estimated at 0.2, 0.4, 0.6, and 0.8 g L−1 of inoculum concentration under autotrophic and heterotrophic modes. The highest removal efficiency of 98.9% and 99.9% was obtained at 0.8 g L−1 inoculum concentration under autotrophic and heterotrophic modes of operation respectively. On increasing the metal concentration from 5 to 10 ppm, a decrease in the biomass quality and quantity was observed. This increased metal stress resulted in an enhanced carbohydrate and lipid concentration. However, the protein concentration was found to be decreased. Cr (VI) removal efficiency was highest in heterotrophic conditions. This study provides a novel insight for removing Cr (VI) and concomitantly enhancing the lipid content of isolated strains under heterotrophic conditions. Owing to enhanced lipid content, the reported curation of wastewater may also be used in harnessing the potential of isolated microalga in biofuel production.

Genetic Variability in Fusarium Wilt Resistant BC2F1 Lines of Yard Long Bean (Vigna unguiculata subsp. sesquipedalis (L.) Verdcourt) Variety Githika for Yield and its Related Traits

Yardlong bean is a highly popular and profitable vegetable traditionally cultivated in the humid tropics of Kerala and is an inexpensive source of vegetable protein. Developing sustainable host plant resistance mechanisms, such as using the backcross method to introduce single-gene resistance into susceptible crop varieties, is crucial for reducing losses due to Fusarium wilt. In the present study Thirty-five resistant backcross progenies (BC2F1) of Yardlong Bean (Vigna unguiculata subsp. sesquipedalis (L.) Verdcourt) variety Githika were evaluated for various genetic parameters for yield and its related traits using Completely Randomized Design at Department of Genetics and Plant Breeding, College of Agriculture, Vellayani, Trivandrum. High phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were observed for traits such as the number of pods per plant, pod weight, pod yield per plant, crude protein content. High Heritability(H) and High Genetic advance as percent mean (GAM) were observed for traits such as Number of pods per plant, Pod weight, Pod length, Number of seeds per pod, Pod Yield per plant, Crude fibre and Crude protein content. This significantly suggest that additive gene action is predominant in these traits and low reliance by environmental factors on these traits, making such character a good candidate for direct selection. The study helps in assessing genetic variability to identify traits as well as lines with maximum similarity with recurrent parent which could be used for further crop improvement program.

Major Histocompatibility Complex Class II Genes Allele Diversity in Landlocked Seals.

The allelic diversity of exon 2 (DQB gene) and exon 3 (DRB gene) of major histocompatibility complex class II was studied for the first time in two species of the landlocked pinnipeds, Baikal (N = 79) and Caspian (N = 32) seals, and these were in compared with the widespread Arctic species, the ringed seal (N = 13). The analysis of the second exon comprising the antigen-binding region revealed high allelic diversity in all three species but the pattern of the diversity was the most specific for the Baikal seal. This species differs from the other two by the smallest number of alleles in the population, yet they have the largest number of alleles per individual and by the maximum similarity of individual genotypes. Presumably, this specificity is a consequence of the spatial and temporal homogeneity of the Lake Baikal environment. Analysis of the third exon encoding the conserved β2-domain showed that the Baikal seal differs by the greatest number of amino acid sequences per individual, while the Caspian seal has the lowest number of variants. A single variant of the β2-domain, the same as in the ringed seal, predominates in the Caspian seal, whereas in the Baikal seal the two other variants predominate. At the same time, three species-specific amino acid sequences were observed among minor variants in the Caspian seal, while only one was found in the Baikal seal. This fact may suggest a longer period of independent evolution in the Caspian seal compared to the Baikal seal.

Facial emotion recognition using deep quantum and advanced transfer learning mechanism.

Facial expressions have become a common way for interaction among humans. People cannot comprehend and predict the emotions or expressions of individuals through simple vision. Thus, in psychology, detecting facial expressions or emotion analysis demands an assessment and evaluation of decisions for identifying the emotions of a person or any group during communication. With the recent evolution of technology, AI (Artificial Intelligence) has gained significant usage, wherein DL (Deep Learning) based algorithms are employed for detecting facial expressions. The study proposes a system design that detects facial expressions by extracting relevant features using a Modified ResNet model. The proposed system stacks building-blocks with residual connections and employs an advanced extraction method with quantum computing, which significantly reduces computation time compared to conventional methods. The backbone stem utilizes a quantum convolutional layer comprised of several parameterized quantum-filters. Additionally, the research integrates residual connections in the ResNet-18 model with the Modified up Sampled Bottle Neck Process (MuS-BNP), retaining computational efficacy while benefiting from residual connections. The proposed model demonstrates superior performance by overcoming the issue of maximum similarity within varied facial expressions. The system's ability to accurately detect and differentiate between expressions is measured using performance metrics such as accuracy, F1-score, recall, and precision. This performance analysis confirms the efficacy of the proposed system, highlighting the advantages of quantum computing in feature extraction and the integration of residual connections. The model achieves quantum superiority, providing faster and more accurate computations compared to existing methodologies. The results suggest that the proposed approach offers a promising solution for facial expression recognition tasks, significantly improving both speed and accuracy.

Muscle synergies during the walk-run and run-walk transitions.

Muscular synergies could represent the patterns of muscular activation used by the central nervous system (CNS) to simplify the production of movement. Studies in walking-running transitions described up to nine synergy modules, and an earlier activation of flexor and extension ankle muscular groups compared to running or walking. Our project aims to study the behaviour of muscle synergies in different stance and swing variations of walking-running (WRT) and running-walking (RWT) transitions. Twenty-four trained men participated in this study. A variable speed protocol on a treadmill was developed to record the activity of 14 muscle during walking, running and relative transitions. The protocol was based on five ramps of 50 seconds each around±10 and 20% of the WRT speed. WRT and RWT were identified according to an abrupt change of the duty factor. Analysing surface electromyography using non-negative matrix factorization (NMF) we obtained synergy modules and temporal activation profiles. Alpha threshold for statistical tests set at 0.05. We described four different transition strides, two for increasing speed transitions, and two for decreasing speed transitions. Four to six synergy modules were found in each condition. According to the maximum cosine similarity results, the two identified WRT conditions shared five modules, while the two RWT conditions shared four modules. WRT and RWT overall shared 4.33±0.58 modules. The activation profiles and centres of activation revealed differences among conditions. Transition occurred at step level, and transition strides were composed by walk-like and run-like steps. Compared with previous studies in running and walking, both transitions needed earlier activation of a comparable number of synergy modules. Synergies were affected by acceleration: during RWT the need to dissipate energy, to decrease the speed, was achieved by increasing the number of co-activating muscles. This was reflected in fewer synergy modules and different activation profiles compared to WRT. We believe that our results could be enforced in different applied fields, like clinical gait analysis, physiotherapy and rehabilitation, where plans including co-activation of specific muscular groups could be useful. Gait transitions are common in different sports, and therefore also application in training and sport science would be possible.

Global and local semantic enhancement of samples for cross-modal hashing

Hashing becomes popular in cross-modal retrieval due to its exceptional performance in both search and storage. However, existing cross-modal hashing (CMH) methods may (a) neglect to learn sufficient modal-specific information, and (b) fail to fully exploit sample semantics. To address these issues, we propose a method called Semantic Enhancement of Sample Hashing (SESH). First, SESH employs a global modal-specific learning strategy to draw overall shared information and global modal-specific information by factoring the mapping matrix. Second, SESH introduces manifold learning and a local modal-specific learning strategy to extract additional local modal-specific and modal-shared data under label guidance. Meanwhile, local modal-specific information is integrated with global modal-specific details to add rich modal-specific information. Third, SESH uses discrete maximum similarity and orthogonal constraint transformation to enhance both global and local semantic information, embedding more discriminative information into the Hamming space. Finally, an efficient discrete optimization algorithm is proposed to generate the hash codes directly. Experiments on three datasets demonstrate the superior performance of SESH. The source code will be available at https://github.com/kokorording/SESH.

Comparative metabolomics in gauging the quality of Embelia ribes Burm. f. fruits in the market trade

Adulteration and substitution in the market trade is a huge problem. Embelia ribes (ER) fruits are used as condiments and in drug formulations that claim substantial market demands. We assessed the trade of ER fruits across Maharashtra, India, for authentication and quality assessment. MaturaseK amplification identified ER’s close relatives E. tsjeriam-cottam (ET) and E. vestita (EV) (Primulaceae), and distant genera Basella alba (BA) (Basellaceae) and Cinnamomum aromaticum (CA) (Lauraceae) fruits in the market trade. Liquid chromatography-tandem mass spectrometry-based comparative metabolomics revealed that ER fruits are metabolically diverse than others. Multivariate analysis showed that ET shares maximum compound similarity with ER and possesses all ER’s major compound classes. However, the percentage of benzoquinones, flavonoids, and furans is less in ET than in ER. Moreover, ER and ET metabolites are associated with different pathways. We conclude that although ET is deemed a good ER substitute, ER replacement by ET altered chemical synergy.

Evaluation of the probiotic ability of Cellulolytic Bacillus subtilis SBMP4 (FSP20) isolated from waste-dump site in FIIRO, Lagos Nigeria

BACKGROUND: Current invention in the field of biotechnology includes research into the probiotic potentials of enzyme-producing bacteria species. OBJECTIVE: This study evaluated the probiotic ability of cellulolytic Bacillus subtilis SBMP4 isolated from waste dump site. METHODS: Isolated Bacillus species was screened for cellulolytic ability on carboxyl methylcellulose agar (CMCA), and hydrolysis efficiency was calculated. Probiotic ability of cellulolytic Bacillus species was carried out as follows: pathogenic potentials, bacteriocin producing ability, antibiotic susceptibility testing, gastric acid, and bile tolerance. Molecular identification was done by means of 16S rRNA gene sequencing, blast search analysis and phylogenetic analysis. RESULTS: Zone of clearance of 30 ± 2.08 mm on CMCA with hydrolysis efficiency of 114 % depicted the isolate’s cellulolytic potential. Bacillus subtilis SBMP4 showed γ-hemolytic phenotype with negative lecithinase activity. Antibacterial activity of bacteriocin observed against Staphylococcus aureus ATCC 25923 reference culture, had maximum inhibition zone of 14±1.0 mm at 72 hour of incubation. Substantial susceptibility towards 9 out of 10 commonly used antibiotics further depicted the isolate’s probiotic ability. Bacillus subtilis SBMP4 exhibited good gastric acid tolerance (pH 2.5) of 5.857 Log cycle/ml and good tolerance to 5 % and 10 % bile salt concentrations with 6.134 and 6.301 Log cycle/ml of viable bacteria cells. Molecular identification of the selected bacteria species revealed it to be Bacillus subtilis SBMP4 with RefSeq accession number NR_118383, showing maximum sequence similarity of 98%. CONCLUSION: Cellulolytic Bacillus subtilis SBMP4 possesses good probiotic ability and has the potentials of being applied as additive in livestock feeding to enhance overall productivity. KEYWORDS: Cellulolytic, Probiotic, CelA gene, Bacteriocin, Bacillus subtilis

Morphological and molecular diversity analysis vis-a-vis phylogenetic correlation among pomegranate (Punica granatum L.) varieties

Abstract Identification of different pomegranate (Punica granatum L.) genotypes using morphological descriptors and further substantiated through molecular means is essential for a breeding program. Characterization using molecular markers has an advantage over its morphological counterpart due to its low expense, quick and reliable results, and the requirement of less sophisticated equipment. In this study, eight different pomegranate cultivars were characterized based on their morphological and molecular characters in sub-tropical regions between 2018 and 2019. Morphological experiments were conducted in a randomized block design replicated thrice for each cultivar, whereas RAPD markers were employed for molecular studies. Principal component analysis revealed significant morphological variation (79.61%) among the cultivars. A high level of polymorphism, similar to PCA, was observed with RAPD analysis (80.49%). Some discrepancies between morphological and molecular results were noted in UPGMA cluster analysis. However, eight primers (total = 41) distinctively separated the Bhagwa variety from others, with the maximum similarity coefficient value (ca. 0.54) and superior fruit quality distinguished in PCA analysis with the maximum mean Euclidean distance (ca. 16). Hence, RAPD markers could be used along with morphological markers for a quick screening of germplasms in further crop improvement programs.

Fourier analysis for detecting vegetation in hyperspectral images

Introduction: hyperspectral images, unlike conventional images, are composed of numerous channels that provide detailed information about the spectral signatures of objects. This allows for the identification of the materials that make them up, and given their potential for detecting environmental changes, identifying vegetation in urban settings using effective computational methods becomes relevant.Objective: the objective of this research is to propose a computational method based on Fourier analysis for detecting vegetation in hyperspectral images.Methods: the research was developed in four methodological phases: selection of technologies, acquisition of the characteristic vegetation pixel, determination of phase similarity between the characteristic pixel and vegetation and non-vegetation pixels, validation of the method on a test hyperspectral image. A method was implemented using the spectral and numpy libraries in Python.Results: the Fourier analysis yielded an average phase similarity of 89.89% and a minimum similarity of 64.54% between the characteristic vegetation pixel and 100 training vegetation pixels. For non-vegetation pixels, the average phase similarity was 42.19%, with a maximum similarity of 63.98%. These results indicate that the proposed method successfully differentiates between vegetation and non-vegetation pixels.Conclusion: the results demonstrate that the Fourier-based method can accurately identify vegetation areas in hyperspectral images, showing non-overlapping phase similarities between vegetation and non-vegetation. This validates the effectiveness of the proposed approach in detecting vegetation in urban environments.

New Extracted Features to Recognize Faces Effected by Occlusions and Common Variations

Face recognition from non-identical face photos is a prominent area of research in pattern recognition and computer vision. Existing face recognition systems struggle with diverse changes like lighting conditions, expressions, and facial occlusions. This paper proposes a new Face Recognition (FR) approach that combines the Elastic Bunch Graph Matching (EBGM) approach with the greedy algorithm to automatically identify face landmarks. The proposed approach independently selects each optimal landmark of face image from different corresponding face images where the corresponding landmark of corresponding face image which achieves the best similarity is used rather than using one or at most two corresponding face images and computing the average between both. The locations of corresponding landmarks can be displaced to achieve maximum similarity with optimal landmarks. This proposed approach demonstrates improved recognition performance compared to contemporary face recognition methods. It effectively handles changing ratios of face parts and can recognize faces even with increasing occlusion sizes.

Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods.

Research on the railway multi-source homonymous geographical entity matching algorithm based on dynamic time warping

This study aims to explore an efficient technique for matching multisource homonymous geographical entities in railways to address the identification issues of homonymous geographical entities. Focusing on railway line vector spatial data, this research investigates the matching problem of multisource homonymous geographical entities. Building on statistical feature matching of attribute data, a curve similarity calculation method based on the DTW algorithm is designed to achieve better local elastic matching, overcoming the limitations of the Fréchet algorithm. The empirical study utilizes railway line layer data from two data sources within Beijing’s jurisdiction, fusing 6237 segment lines from source 2 with 105 long lines from source 1. The structural comparison between the two data sources is conducted through statistical methods, applying cosine similarity and the maximum similarity value of TF-IDF for text similarity calculation. Finally, Python is used to implement the DTW algorithm for curve similarity. The experimental results show an average DTW distance of 3.92, a standard deviation of 4.63, and a mode of 0.005. Similarity measurement results indicate that 95.53% of records are within the predetermined threshold, demonstrating the effectiveness and applicability of the method. The findings significantly enhance the accuracy of railway data matching, promoting the informatization of the railway industry, and hold substantial significance for improving railway operational efficiency and system performance.

Identification of hub genes and potential networks by centrality network analysis of PCR amplified Fusarium oxysporum f. sp. lycopersici EF1α gene

BackgroundFusarium wilt is a devastating soil-borne fungal disease of tomato across the world. Conventional method of disease prevention including usage of common pesticides and methods like soil solarisation are usually ineffective in the treatment of this disease. Therefore, there is an urgent need to identify virulence related genes in the pathogen which can be targeted for fungicide development.ResultsPathogenicity testing and phylogenetic classification of the pathogen used in this study confirmed it as Fusarium oxysporum f. sp. lycopersici (Fol) strain. A recent discovery indicates that EF1α, a protein with conserved structural similarity across several fungal genera, has a role in the pathogenicity of Magnaporthe oryzae, the rice blast fungus. Therefore, in this study we have done structural and functional classification of EF1α to understand its role in pathogenicity of Fol. The protein model of Fol EF1α was created using the template crystal structure of the yeast elongation factor complex EEF1A:EEF1BA which showed maximum similarity with the target protein. Using the STRING online database, the interactive information among the hub genes of EF1α was identified and the protein–protein interaction network was recognized using the Cytoscape software. On combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 4 hub genes including Fol EF1α were selected for further investigation. The three interactors of Fol EF1α showed maximum similarity with homologous proteins found in Neurospora crassa complexed with the known fungicide, cycloheximide. Through the sequence similarity and PDB database analysis, homologs of Fol EF1α were found: EEF1A:EEF1BA in complex with GDPNP in yeast and EF1α in complex with GDP in Sulfolobus solfataricus. The STITCH database analysis suggested that EF1α and its other interacting partners interact with guanosine diphosphate (GDPNP) and guanosine triphosphate (GTP).ConclusionsOur study offers a framework for recognition of several hub genes network in Fusarium wilt that can be used as novel targets for fungicide development. The involvement of EF1α in nucleocytoplasmic transport pathway suggests that it plays role in GTP binding and thus apart from its use as a biomarker, it may be further exploited as an effective target for fungicide development. Since, the three other proteins that were found to be tightly associated Fol EF1α have shown maximum similarity with homologous proteins of Neurospora crassa that form complex with fungicide- Cycloheximide. Therefore, we suggest that cycloheximide can also be used against Fusarium wilt disease in tomato. The active site cavity of Fol EF1α can also be determined for computational screening of fungicides using the homologous proteins observed in yeast and Sulfolobus solfataricus. On this basis, we also suggest that the other closely associated genes that have been identified through STITCH analysis, they can also be targeted for fungicide development.

The Comparative Full-Length Genome Characterization of African Swine Fever Virus Detected in Thailand.

African swine fever virus (ASFV) has been responsible for the globally devastating epidemics in wild and domesticated pigs. Of the 24 identified ASFV genotypes, genotype II is the primary cause for the pandemic occurring in Europe and Asia since its emergence in Georgia in 2007. The current study aimed to characterize the full-length genomic pattern of the ASFV strain from Thailand, TH1_22/CR (Accession No. PP915735), which was then compared with genomic diversity across other Asian isolates using Georgia 2007/1 (Accession No. FR682468) as the reference. Viral DNA was isolated from the pig spleen sample following library preparation and paired-end sequencing using the MiSeq Illumina platform. The sequenced TH1_22/CR isolate spanned 189,395 nucleotides encoding 193 open reading frames (ORFs), exhibiting maximum nucleotide similarity (99.99%) with Georgian (Georgia 2007/1) and Chinese (Wuhan 2019-1 and China HLJ) isolates. Based on phylogenetic analysis, the TH1_22/CR isolate (Accession No. PP915735) was characterized as genotype II, serogroup 8, and IGR-II due to the presence of three tandem repeat sequences (TRSs). Genetic variations including SNPs and single and polynucleotide indels were identified in TH1_22/CR in agreement with other Asian isolates. For comprehensive analysis, the genome was divided into four regions (I-IV) based on gene location. Overall, the TH1_22/CR isolate demonstrated eight SNPs and indels in its genome. Two unique SNPs were reported in the coding regions of the TH1_22/CR isolate, out of which, a C-591-T substitution was seen in MGF 360-4L and a C-297-T was found in A238L, and four unique SNPs were reported in non-coding regions (NCRs). Furthermore, a 29 bp deletion was observed in the IGR between MGF 110-13La and MGF 110-13Lb, as well as 52 bp deletion in the ASFV G ACD 00350 gene. This comparative analysis establishes the foundational information for future studies on the diversity and phylogeography of this regionally significant genetic sub-group of ASFV.

Utilization of opium poppy seed oil for biodiesel production: A parametric characterization and statistical optimization

Consuming traditional petroleum-derived diesel fuel has long been associated with issues such as the depletion of natural energy resources. To solve these challenges, an alternate source like as biodiesel is an appealing option. Seed oils have long been recognized as an abundant and diverse source of biodiesel. In this study, poppy seed oil from the poppy (Papaver somniferum) was investigated for biodiesel production. Poppy seed biodiesel was generated and refined using acid-pretreated esterification with sulphuric acid prior to transesterification, as well as single-step alkaline catalyzed transesterification with methanol and potassium hydroxide. Finally, the percentage yield was compared. Using Statistica, the Box-Behnken design was applied to optimize process variables like time, temperature, catalyst concentration, and methanol-oil ratio to produce maximum yield. The relationship of process variables was also shown with the help of the Response Surface Methodology. A maximum yield of 94.87 % was obtained at optimized conditions, i.e., 90min reaction time, 60 °C of temperature, 0.25 mg of catalyst concentration, and 3v/v% alcohol-oil ratio. The fuel properties of biodiesel produced, such as acid value, moisture content, saponification value, iodine value, specific gravity, percentage of free fatty acids, refractive index, viscosity, boiling point, and peroxide value, were measured and compared with the American Society for Testing and Materials (ASTM) D6751 and European Standards (EN) 14214. Further results were studied and discussed using Fourier Transfer Infrared (FTIR) analysis, which showed maximum similarity of raw material to formed biodiesel. Gas Chromatography-Mass Spectrometry (GC-MS) analysis was performed to identify and quantify various fatty acid methyl esters. The results obtained were in accordance with various international standards for biodiesel fuel. Thus, poppy seeds can be used to obtain biodiesel.

The effect of the tidal cycle on the phytoplankton community assemblage in a mangrove-dominated tropical tidal creek.

A comparative assessment of phytoplankton dynamics during low tide (LT) and high tide (HT) was conducted from February 2022 to January 2023 in a tropical mesotidal creek, Manori, Mumbai, India. In total, 124 phytoplankton species were recorded. The HT resulted in greater species richness (124 species) and diversity indices (Shannon-Wiener's index) than the LT (102 species). The Pielou's evenness (J') and Simpson's dominance index (1-D) did not show significant fluctuations with the tides due to the marine phytoplankton species moving rhythmically in and out of the creek with the tides. Overall, the seasonal abundance was maximum during pre-monsoon at HT (5.79 × 103 u/L) and lowest in monsoon at LT (0.45 × 103 u/L), whereas spatial abundance was maximum at S1 (HT- 4.04 × 103 u/L) at HT and lowest at S3 (LT- 0.75 × 103 u/L) at LT. The diatoms dominated in their abundance (1.83 × 103 u/L and 3.82 × 103 u/L in HT) and diversity (77 in LT and 92 in HT). The species such as Coscinodiscus centralis, Coscinodiscus granii, Coscinodiscus radiatus, Triops furca, Melosira varians, Nitzchsia palea, Chaetoceros affinis, Skeletonema marinoi, Stephanocyclus meneghinianus, Planktoniella sol and Skeletonema costatumwere the dominant native residents in the creek. SIMPER analysis revealed that the maximum similarity was during the monsoon (47.65%), and the minimum was during the pre-monsoon (38.10%) at LT. However, in HT, the maximum similarity of phytoplankton shifted to post-monsoon (63.85%) and the minimum during the pre-monsoon (46.71%). The mean value of richness (d') and Shannon's diversity (H') showed a moderate phytoplankton diversity in the system. The environmental parameters (water temperature > pH > nitrate > salinity > DO > Alk > silicate) have a greater influence on the distribution of the phytoplankton community with tides as revealed by the Canonical correspondence analysis. Therefore, it has been found that tides play a significant role in the distribution and abundance of the phytoplankton community in a mesotidal creek environment.

Rainfall impacts on urban route choices by private vehicle users: insights from São Paulo, Brazil

Urban drivers frequently experience challenges posed by adverse weather conditions like heavy rain. Despite the influence of these conditions on individual travel behaviour, there is limited understanding of how various aspects of rainfall affect the route choices in urban trips. In this context, this paper aims to evaluate the impacts of different rainfall conditions on the route choice behaviour of private vehicle users. We propose a method to identify the specific routes taken by drivers, utilizing observed trips reproduced from a large-scale dataset containing license plate information recognized by speed device cameras. Path Size Multinomial Logit models penalizing routes that share common links were estimated based on a choice set comprised of routes with a maximum similarity threshold. Distance and the actual travel times in the routes were estimated using real-world data from a third-party company, and cumulative, average and maximum rainfall through each route of the choice set were obtained from data captured by meteorological radar. A case study in the city of São Paulo, Brazil, considered the average and maximum rainfall intensities, distance, and travel time in different models. The results indicate that both average and maximum rainfall intensities negatively impact the utility of routes. This research enables the identification of routes with the highest probabilities of being chosen during intense rainfall. Such information is valuable for implementing measures to minimize the impacts caused by adverse weather conditions in urban trips.

An intelligent compound fault diagnosis method using generalized zero-shot model of bearing

Abstract Compound fault occurrence has been unpredictable, especially in industrial scenarios where it is difficult to collect a large number of labeled samples for compound fault. Based on this, this paper proposes a generative generalized zero-shot learning(GZSL) model aimed at synthesizing compound fault features through training with single fault samples. These synthesized features are then used for the recognition of compound fault. Firstly, in order to construct an accurate and effective semantic vector, the semantic generation module and discriminator are utilized to generate the semantics of compound fault. Secondly, a feature extraction module based on CNN is designed to extract various fault features from the two-dimensional time-frequency diagram. Finally, a fault semantic matching module is designed to match the feature vectors of compound faults with the generated fault semantic vectors. This enables the identification of unseen compound fault by computing their maximum similarity. The experimental results demonstrate that the proposed method achieved H scores of 75.83 and 69.24 on two real fault datasets, ensuring the correct classification of compound fault to the greatest extent possible.