Analysis Of Patterns Research Articles

Improvement of volatile aromatic compound levels and sensory quality of distilled soju derived from Saccharomyces cerevisiae and Wickerhamomyces anomalus co-fermentation

Distilled soju, a Korean traditional alcoholic beverage, is produced by fermenting rice with a variety of microorganisms including molds, yeasts, and lactic acid-producing bacteria, followed by distillation. Our study sought to improve the quality of distilled soju through co-fermentation with Saccharomyces cerevisiae and Wickerhamomyces anomalus, known for producing volatile aromatic compounds during the early stages of fermentation. Analysis of volatile aromatic compounds in co-fermented distilled soju revealed a substantial increase in compounds with boiling points below 200 °C. Among them, ethyl hexanoate, isobutanol, and isoamyl alcohol were identified as the major volatile aromatic compounds based on Log2 fold change analyses of the volatile aromatic compound pattern. In sensory evaluation, co-fermented distilled soju received higher scores in terms of odor and overall preference. Therefore, incorporating W. anomalus may improve the quality of distilled soju.

Analysis of the Burial Depth Pattern of Shallow Groundwater Based on Wavelet and Hilbert-Huang Transform Methods

Analysis of the Burial Depth Pattern of Shallow Groundwater Based on Wavelet and Hilbert-Huang Transform Methods

Probabilistic Forecasting of Regional Solar Power Incorporating Weather Pattern Diversity

Power grid stability depends on the ability to forecast solar power generation at the regional level. Most previous research on probabilistic forecasting has focused on the use of machine learning to predict the output of individual solar power plants rather than regional solar power generation, and few studies have considered the effects of seasonal weather patterns at the regional level. In this study, climate and geographic data were collected from 83 weather stations between 2019 and 2021 for use in developing a probabilistic model by which to predict regional solar power generation. The results of weather pattern analysis based on unsupervised machine learning and ensemble voting were used to build a probabilistic quantile regression model for the short-term prediction of regional solar power generation capacity. The efficacy of the model was assessed using data from 48 solar PV power plants, which included four sub-datasets pertaining to four target regions. Highly accurate results were consistently obtained across all regions in both the winter and summer seasons. The proposed probabilistic model outperformed conventional deterministic model by 6.55% and conventional probabilistic model by 4.03% in terms of total normalized mean absolute error (NMAE). Prediction intervals generated by the proposed model could be used as input parameters for regional power dispatch decisions.

How to restore ecological impacts from wind energy? An assessment of Zhongying Wind Farm through MSPA-MCR model and circuit theory

The wind energy industry is developing rapidly in China with a maximum capacity worldwide. Meanwhile, the stability of the ecological network is challenged by extensive renewable energy expansion. There are still research deficiencies in wind farm ecological impacts at project scale from an ecological restoration perspective. It is of great necessity to monitor the ecological impacts with time going and explore ecological restoration strategies for wind power development. The research case of Zhongying Wind Farm is selected based on investigation and interviews regarding its ecological impact in the prior study. It employs a comparative methodology by comparing the ecological quality before and after the construction of Zhongying Wind Farm. Raw data from Landsat 8, the Digital Elevation Model (DEM), the Normalized Difference Vegetation Index (NDVI), and wind turbine statistics originating from open data platforms are analyzed and visually represented with ArcGIS. This paper establishes an ecological network based on morphological spatial pattern analysis (MSPA) and the Minimum Cumulative Resistance (MCR) model to quantify wind farms’ impacts on ecological corridors. The circuit theory and Pajek software are used to identify and categorize the ecological nodes into weak and obstacle points. The results reveal that ecological impacts on ecological corridors decay with distance, reflecting changes in length, connection strength and cumulative values of corridors. While the impacts on ecological nodes depend on nodes’ attributes: the number of weak points increased by 18.5 %, and obstacle points increased by 350 %, with their locations shifting from the edges of urban construction sites to the edges of wind farm. Differentiated strategies are proposed according to the impact magnitude of each ecological corridor and node: such as dynamic inspection mechanism, construction prohibition for protection; corridor structure and function recovering, stepping stones addition for restoration; buffer belts, warning radar, and monetary schemes for compensation. This research fills the gap of ecological impact assessment under the challenges of wind energy expansion. It helps to balance wind energy development and ecological restoration at the regional level.

Nonfrontal Control of Working Memory.

Items held in visual working memory can be quickly updated, replaced, removed, and even manipulated in accordance with current behavioral goals. Here, we use multivariate pattern analyses to identify the patterns of neuronal activity that realize the executive control processes supervising these flexible stores. We find that portions of the middle temporal gyrus and the intraparietal sulcus represent what item is cued for continued memorization independently of representations of the item itself. Importantly, this selection-specific activity could not be explained by sensory representations of the cue and is only present when control is exerted. Our results suggest that the selection of memorized items might be controlled in a distributed and decentralized fashion. This evidence provides an alternative perspective to the notion of "domain general" central executive control over memory function.

Detecting vulnerabilities in website using multiscale approaches: based on case study

In the realm of modern web applications, security stands as an utmost priority. To address this critical concern, we've developed a versatile Python script with the primary goal of proactively identifying vulnerabilities and thwarting transient attacks. Leveraging various libraries, this tool comprehensively covers a broad spectrum of threats, including SQL injection (SQLi), cross-site scripting (XSS), cross-site request forgery (CSRF), sensitive data leakage, security misconfiguration, distributed denial-of-service (DDoS) vulnerabilities, and secure socket layer (SSL) or transport layer security (TLS). This Python-based solution prioritizes adaptability, ensuring seamless integration of future updates to effectively combat evolving threats. Utilizing innovative methods such as SQLi and XSS payload injection, the script assesses the susceptibility of input fields. And addressing CSRF vulnerabilities, the script generates and validates tokens, fortifying defenses against unauthorized actions. Employing pattern analysis, it combats sensitive data exposure and security misconfigurations, adeptly identifying elements like credit card numbers, passwords, and headers. Furthermore, the script enhances overall security by scrutinizing SSL/TLS protocols and monitoring port accessibility. It reinforces DDoS detection by actively monitoring traffic patterns, identifying anomalies, and proactively averting disruptions.

Roles of the Default Mode Network in Different Aspects of Self-representation When Remembering Social Autobiographical Memories.

Autobiographical memory (AM) is episodic memory for personally experienced events, in which self-representation is more important than that in laboratory-based memory. Theoretically, self-representation in a social context is categorized as the interpersonal self (IS) referred to in a social interaction with a person or the social-valued self (SS) based on the reputation of the self in the surrounding society. Although functional neuroimaging studies have demonstrated the involvement of the default mode network (DMN) in self-representation, little is known about how the DMN subsystems contribute differentially to IS-related and SS-related AMs. To elucidate this issue, we used fMRI to scan healthy young adults during the recollection of AMs. We performed multivariate pattern analysis (MVPA) and assessed functional connectivity in the DMN subsystems: the midline core, medial temporal lobe (MTL), and dorsomedial pFC (dmPFC) subsystems. The study yielded two main sets of findings. First, MVPA revealed that all DMN subsystems showed significant classification accuracy between IS-related and nonsocial-self-related AMs, and IS-related functional connectivity of the midline core regions with the retrosplenial cortex of the MTL subsystem and the dmPFC of the dmPFC subsystem was significant. Second, MVPA significantly distinguished between SS-related and nonsocial-self-related AMs in the midline core and dmPFC subsystems but not in the MTL subsystem, and SS-related functional connectivity with the midline core regions was significant in the temporal pole and TPJ of the dmPFC subsystem. Thus, dissociable neural mechanisms in the DMN could contribute to different aspects of self-representation in social AMs.

Unveiling Crash Casualties: A Neural Network Model for Crash Type Classification Using Traffic Datasets

Abstract: Traffic accidents, a significant public health burden with substantial economic costs, necessitate proactive measures for sustainable transportation. This study investigates the efficacy of neural networks in predicting crash types using traffic data. We propose a deep learning model that leverages the power of recurrent neural networks (RNNs) for sequential traffic pattern analysis and convolutional neural networks (CNNs) for extracting spatial features from traffic data. The model, trained on a labeled traffic dataset, learns to identify patterns and relationships between traffic characteristics (e.g., speed, volume, weather) and different crash types (e.g., rear-end collision, single-vehicle). The evaluation focuses on the model's accuracy in predicting crash types using metrics like precision, recall, and F1-score. This research contributes to the development of intelligent traffic management systems (ITMS) that can proactively identify and mitigate potential accidents, ultimately enhancing road safety and promoting a more sustainable transportation future.The implementation results indicate that the proposed method yields an accuracy of 82.93% approximately.

Anatomical Patterns of Nodal Spread in Unilateral Papillary and Medullary Thyroid Cancer.

Background: Skip metastases, node metastases in the lateral neck sparing the ipsilateral central neck, challenge the current concept of central-to-lateral lymphatic spread. This study sought to delineate patterns of central and lateral neck involvement in unilateral papillary thyroid cancer (PTC) and medullary thyroid cancer (MTC). Methods: This was a retrospective correlative analysis of nodal patterns in surgical specimens from patients with unilateral PTC or MTC who had undergone thyroidectomy with at least ipsilateral central neck dissection between November 1994 and January 2024 at a tertiary referral center. Results: Included were 833 patients with unilateral PTC and 640 patients with unilateral MTC. Simultaneous presence or absence of node metastases was noted in ipsilateral central and lateral neck compartments in 76.6-78.1% of patients with PTC (both node positive in 27.0-54.7% and both node negative in 23.4-49.6%) and 77.3-80.0% of patients with MTC (both node positive in 26.6-33.2% and both node negative in 44.1-53.4%). Only one ipsilateral neck compartment was node positive in 21.9-23.4% of patients with PTC and 20.0-22.7% of patients with MTC. The ipsilateral central, but not the ipsilateral lateral compartment, was node positive in 8.8-16.9% with PTC and 8.6-8.8% of patients with MTC, whereas the ipsilateral lateral, but not the ipsilateral central compartment, was node positive in 6.5-13.1% with PTC and 11.3-14.1% with MTC. Ipsilateral lateral neck involvement sparing the ipsilateral central neck was 1.5-2 times more frequent in patients with node positive MTC than patients with node positive PTC (24.2-25.2% vs. 12.9-17.1%). Greater numbers of node metastases in the ipsilateral central neck compartment were associated with more frequent involvement of the ipsilateral lateral, contralateral central, and contralateral lateral neck compartments. Thyroid tumor diameter intensified nodal spread without changing nodal spread patterns. Conclusions: These histopathological findings, which need to be interpreted in light of the respective tumor biology, offer an unprecedented glimpse at the metastatic patterns of unilateral PTC and MTC. Customizing neck dissection to the patterns of nodal spread, considering operative status (initial vs. reoperative surgery) and experience with neck dissection, may require more frequent concomitant dissections of ipsilateral central and ipsilateral lateral neck compartments.

Assessing urban growth through morphological spatial pattern analysis in cloud computing platform

PurposeThis study aims to quantify and analyse the dynamics of land use and land cover (LULC) changes over three decades in the rapidly urbanizing city of Abha, Saudi Arabia, and to assess urban growth using Morphological Spatial Pattern Analysis (MSPA).Design/methodology/approachUsing the Support Vector Machine (SVM) classification in Google Earth Engine, changes in land use in Abha between 1990 and 2020 are accurately assessed. This method leverages cloud computing to enhance the efficiency and accuracy of big data analysis. Additionally, MSPA was employed in Google Colab to analyse urban growth patterns.FindingsThe study demonstrates significant expansion of urban areas in Abha, growing from 62.46 km² in 1990 to 271.45 km² in 2020, while aquatic habitats decreased from 1.36 km² to 0.52 km². MSPA revealed a notable increase in urban core areas from 41.66 km² in 2001 to 194.97 km² in 2021, showcasing the nuanced dynamics of urban sprawl and densification.Originality/valueThe novelty of this study lies in its integrated approach, combining LULC and MSPA analyses within a cloud computing framework to capture the dynamics of city and environment. The insights from this study are poised to influence policy and planning decisions, particularly in fostering sustainable urban environments that accommodate growth while preserving natural habitats. This approach is crucial for devising strategies that can adapt to and mitigate the environmental impacts of urban expansion.

Studies on the Vascular Flora of Anaimudi and the Surrounding Regions, Kottayam District, Kerala

The paper deals with an account of the vegetation of Anaimudi (2,695 m) - the highest peak in the peninsular India - and the surrounding regions situated in the Kottayam District of Kerala. The above area has not received much attention in the detailed studies undertaken by various workers on the vegetation of the South Indian hill tops. Based on the explorations carried out by the authors during the years 1965-70, 317 taxa of vascular plants, under 233 genera and 101 families, are enumerated. An analysis of the distribution pattern of the shola and grassland species has shown that (1) the shola species are mainly of tropical stock, (2) quite a number of species on the shola fringes and the grassland are of temperate origin and (3) the temperate elements play a comparatively less important role in the composition of the vegetation at the higher altitudes (above 1,925 m), and that the predominent elements here are of tropical stock. The high incidence of endemic taxa in this region has been pointed out. Literature on the ecological status of the shola-grassland formation has been reviewed for a better understanding of the vegetation.

Keratin 19 (Krt19) is a novel marker gene for epicardial cells

Epicardial cells regulate heart growth by secreting numerous growth factors and undergoing lineage specification into other cardiac lineages. However, the lack of specific marker genes for epicardial cells has hindered the understanding of this cell type in heart development. Through the analysis of a cardiac single cell mRNA sequencing dataset, we identified a novel epicardial gene named Keratin 19 (Krt19). Further analysis of the expression patterns of Krt19 and Wt1, a well-known epicardial gene, revealed their preferences in major cardiac cell types. Using lineage-tracing analysis, we analyzed Krt19-CreER labeled cells at multiple time windows and found that it labels epicardial cells at both embryonic and neonatal stages. Furthermore, we studied the function of epicardial cells using a diphtheria toxin A chain (DTA)-based cell ablation system. We discovered that Krt19-CreER labeled cells are essential for fetal heart development. Finally, we investigated the function of Krt19-CreER and Wt1-CreER labeled cells in neonatal mouse development. We observed that the Krt19-CreER; Rosa-DTA mice displayed a smaller size after tamoxifen treatment, suggesting the potential importance of Krt19-CreER labeled cells in neonatal mouse development. Additionally, we found that Wt1-CreER; Rosa-DTA mice died at early stages, likely due to defects in the kidney and spleen. In summary, we have identified Krt19 as a new epicardial cell marker gene and further explored the function of epicardial cells using the Krt19-CreER and Wt1-CreER-mediated DTA ablation system.

Nonparametric testing of the covariate significance for spatial point patterns under the presence of nuisance covariates

Determining the relevant spatial covariates is one of the most important problems in the analysis of point patterns. Parametric methods may lead to incorrect conclusions, especially when the model of interactions between points is wrong. Therefore, we propose a fully nonparametric approach to testing significance of a covariate, taking into account the possible effects of nuisance covariates. Our tests match the nominal significance level, and their powers are comparable with the powers of parametric tests in cases where both the model for intensity function and the model for interactions are correct. When the parametric model for the intensity function is wrong, our tests achieve higher powers. The proposed methods rely on Monte Carlo testing and take advantage of the newly introduced concepts: the covariate-weighted residual measure and nonparametric residuals. We also define a correlation coefficient between a point process and a covariate and a partial correlation coefficient quantifying the dependence between a point process and a covariate of interest while removing the influence of nuisance covariates.

Faith in frames: unveiling therapeutic narratives in religion-related cinema through computational analysis

IntroductionThis study explores the emotional impact of religion-related films through a “cinematherapy” lens. It aims to analyze the emotional patterns in a curated selection of religion-related films compared to a broader sample of acclaimed movies using facial recognition with YOLOv5 object detection. The study aims to uncover the potential therapeutic application of religion-related films.MethodsFacial recognition with YOLOv5 object detection was utilized in this study to analyze the emotional patterns in religion-related films. A curated selection of these films was compared to a broader sample of acclaimed movies to identify any distinct emotional trajectories.ResultsThe analysis of the emotional patterns revealed that religion-related films exhibited a subtler range of emotions compared to the broader film spectrum. This finding suggests that these films potentially create a safe space for contemplation, aligning with the profound themes often explored in religion-related films. Interestingly, the emotional arc observed in the films mirrored the spiritual journeys depicted in them. The films started with a low point of separation, transitioned through challenges, and culminated in a peak representing spiritual transformation.DiscussionThese findings suggest promise for the therapeutic application of religion-related films. The muted emotional expression in these films creates a safe space for self-reflection, enabling viewers to connect with the struggles of the characters and explore their own values when faced with complex religious ideas. This emotional engagement may contribute to therapeutic goals such as introspection and personal growth. The study unveils the unique emotional power of religion-related films and paves the way for further research on their potential as therapeutic tools. It emphasizes the need for continued exploration of the emotional impact of these films and their capacity to aid in therapeutic goals.

Remote intelligent perception system for multi-object detection

IntroductionDuring the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.MethodsAdvances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.ResultsThe performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.DiscussionFurthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

Genetic diversity and haplotype distribution patterns analysis of cytb and RAG2 sequences in Rana hanluica from southern China

Rana hanluica: an endemic amphibian of China, is found in the hills and mountains south of the Yangtze River. In this comprehensive study, we collected 162 samples from 14 different localities to delve into the genetic diversity of Rana hanluica using mitochondrial Cytb and nuclear RAG2 as genetic markers. Our findings reveal that the Nanling Mountains, specifically regions like Jiuyi Shan, Jinggang Shan, Mang Shan, and Qiyun Shan, are genetic hotspots harboring remarkable diversity. The research results also indicate that there is gene flow among the various populations of the species, and no distinct population structure has formed, which may be due to migration. Moreover, populations in some regions, as well as the overall population, show signs of a possible genetic bottleneck, which we speculate may have been caused by climate change. However, given the exploratory nature of our study, further investigations are warranted to confirm these observations. Through phylogenetic analyses, we uncovered indications that R. hanluica might have originated within the Nanling region, dispersing along the east-west mountain ranges, with a significant contribution originating from Jiuyi Shan. The genetic distributions uncovered through our research reflect historical migratory patterns, evident in the distinct haplotypes of the RAG2 gene between the western and eastern parts of the studied area. Moreover, Heng Shan and Yangming Shan exhibited unique genetic signatures, possibly influenced by geographic isolation, which has shaped their distinct genotypes. The insights gained from this study hold profound implications for conservation efforts. By identifying regions rich in genetic diversity and crucial gene flow corridors, we can develop more effective conservation strategies. Preserving these genetically diverse areas, especially within the Nanling Mountains, is vital for maintaining the evolutionary potential of R. hanluica. In conclusion, our research has laid a solid foundation for understanding the genetic landscape of R. hanluica, shedding light on its origins, population structures, and evolutionary trajectories. This knowledge will undoubtedly guide future research endeavors and inform conservation strategies for this endemic amphibian.

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

Face recognition is one of the important elements that can be used for securing the facilities, emotion recognition, sentiment exploration, fraud analysis, and traffic pattern analysis. Intelligent face recognition has yielded excellent accuracy in a controlled environment whereas vice versa in an uncontrolled environment. However, conventional methods can no longer satisfy the demand at present due to their low recognition accuracy and restrictions on many occasions. This study proposed an optimal deep learning-based face recognition system that improves the security of the model developed in the IoT cloud environment. Initially, the dataset of images was gathered from the public repository. The captured images are explored using image processing techniques like image preprocessing employing the Gaussian filter technique for removing the noise and smoothing the image. The histogram of oriented gradients (HOGs) is used for the image segmentation. The processed images are preserved at the cloud service layer. Extract features were linked to facial activities using the spatial-temporal interest point (STIP). On the other hand, the extracted feature vectors are investigated using galactic swarm optimization (GSO) techniques that give optimized feature vectors. The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. This will allow the effectiveness of the GSO-CNN technique to be assessed in terms of recognition accuracy, recall, precision, and error rate.

Towards Real-world Violence Recognition via Efficient Deep Features and Sequential Patterns Analysis

Towards Real-world Violence Recognition via Efficient Deep Features and Sequential Patterns Analysis

In Vitro Probiotic Characterization of Lactiplantibacillus plantarum Strains Isolated from Traditional Fermented Dockounou Paste

This study aimed to evaluate the probiotic properties of 10 lactic acid bacteria (LAB) isolated from artisanal fermented plantain dockounou paste. A preliminary characterization of the LAB isolates was performed based on phenotypic and several biochemical properties, which was subsequently confirmed through 16S rRNA gene sequencing analysis, indicating that these isolates belonged to the species Lactiplantibacillus plantarum. With regard to safety criteria, the strains exhibited no alpha or beta hemolysis activity. Nevertheless, the majority of LAB strains demonstrated high sensitivity to the antibiotics tested. The results demonstrated that the majority of the strains exhibited remarkably high survival rates under simulated gastrointestinal conditions, such as pH = 1.5 (81.18–98.15%), 0.3% bile salts (68.62–100.89%), 0.4% phenol (40.59–128.24%), as well as 0.1% pepsin and pH = 2.5 (88.54–99.78%). The LAB strains demonstrated elevated levels of cell surface properties, indicative of the presence of a considerable defensive mechanism against pathogens. Intact LAB cells exhibited significant antioxidant abilities (48.18–83.58%). They also demonstrated a pronounced inhibitory effect on the growth of foodborne pathogens. Enzyme pattern analysis revealed that the LAB isolates produced both proteases and cellulases, as well as pectinase and/or amylase activity. The potential of the L. plantarum strains FS43, FS44, and FS48, as indicated by the results obtained from the standard in vitro assays, makes them suitable for further study as potential probiotics.

Ecological Security Pattern Construction in Loess Plateau Areas—A Case Study of Shanxi Province, China

Strong soil erosion and increasing human activities have made Loess Plateau areas ecologically fragile regions. Constructing the ecological security pattern (ESP) is imperative to maintain their ecosystem functions and sustainable development. However, it is still challenging to establish the ESP in such an unstable and scattered ecological environment. In this study, we take Shanxi Province, which suffers severe ecological problems in Loess Plateau areas, as an example to construct the ESP in a pattern of “source-resistance-corridor”. The proposed methods include the following steps: (1) potential ecological sources are selected with important ecosystem functions based on contributions of soil and water conservation, habitat quality, and carbon storage; (2) ecological sources are determined by considering core areas at the landscape scale based on morphological spatial pattern analysis (MSPA) along with stability based on dynamic assessment on previous sources; (3) the comprehensive resistance surface is constructed by multiple resistance factors and remotely sensed nighttime light data; (4) ecological corridors are simulated and extracted based on circuit theory. As a result, the proposed ESP in our study area mainly includes 13,592 km2 of ecological sources, 8519.64 km of ecological corridors, and 277 ecological nodes. Meanwhile, an ecological framework of “two axes, three belts, and three zones” was proposed based on the optimization and reorganization of ecological components within the ESP. Our research lays a methodological and practical foundation for regional ESP construction and sustainable development in Loess Plateau areas.