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Background & Objective: Anemia is major public health problem throughout the world and the commonest cause behind it is iron deficiency. Oral iron intake is the treatment of choice, but parenteral administration of iron is necessary under certain circumstances like intolerance to oral iron or pregnancy near term. The objective of present study is to compare the efficacy and safety of parenteral Iron Sucrose to Ferric Carboxymaltose in improving the hematological indices in anemic pregnant women. Materials & Methods: The study was a comparative, observational study conducted at Department of Obstetrics and Gynecology, Shri Guru Ram Rai Institute of Health and Medical Sciences, Dehradun from September 2018 to May 2020. Sample size was 120 pregnant women with iron deficiency anemia in each group. One group was treated with intravenous Iron Sucrose and other group was treated with intravenous Ferric Carboxymaltose after calculation of total iron dose. Hematological tests were repeated at day 14th and 28th of the treatment. Outcome was measured by improvement in hematological parameters and experienced side effects. Data was analyzed by using appropriate statistical tests. Results: According to our study, both groups were comparable in term of sociodemographic features and baseline hematological parameters. Hemoglobin rise was more and rapid in group treated with ferric carboxymaltose (8.7 ± 0.47 g/dl to 11.6 ± 0.77 g/dl) than the iron sucrose (8.24 ± 0.57 g/dl to 10.60 ± 0.87g/dl) group. Rise in serum ferritin and MCV of RBC were also more in ferric carboxymaltose group than the iron sucrose group. Both groups have only minimal and mild side effects, but side effects were more in iron sucrose group than iron carboxymaltose group. Conclusion: Hence we can conclude by our study that parenteral therapy with iron sucrose and carboxymaltose, both are able to successfully treat iron deficiency anemia in pregnancy but the improvement is faster, safer and more convenient with the use of ferric carboxymaltose than iron sucrose. [ABSTRACT FROM AUTHOR]

This study aimed to determine the acute effects of the French contrast method (FCM) and post-activation potentiation (PAP) protocols on 3 × 3 basketball game demands and thermal asymmetry in male basketball players and to compare these effects between protocols. Eighteen male basketball players (mean ± SD; age: 21.7 ± 1.5 years, 10.6 ± 1.9 years of experience) visited the laboratory four times, 72 h apart. The players participated in three different protocols (baseline: 3 × 3 game; FCM + 3 × 3 game; PAP + 3 × 3 game; respectively). The players' internal and external loads were monitored, game profiles were analyzed, and thermography was applied during the protocols. The results revealed that FCM and PAP did not significantly differ in internal load; however, the significant highest total distance and distance in band 2 during the 3 × 3 basketball game was after the FCM. The 1-point attempt was significantly higher after the FCM, and turnover was significantly higher after PAP. Significant thermal asymmetry was observed in the abdominals and lower back after the FCM and PAP. The results of this study provide coaches and practitioners with detailed information regarding the game demands that can be used to improve the playing profile of 3 × 3 basketball players. [ABSTRACT FROM AUTHOR]

Copyright of Modern Urban Transit is the property of Modern Urban Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The shortage of young people in agriculture and the decline of rural areas are increasingly pressing issues that capture the attention of European policymakers and researchers. Despite the low rate of youth involved in agricultural production, recent data reveal a significant proportion of young farmers in Italy that are engaged in geographical indication (GI) production. Statistics provide trend analysis, but they alone are not sufficient in clarifying the motivations behind young people’s decision-making. We conducted a qualitative study of Sicilian youth involved in GI to understand their motivations to pursue GI certification and the implications for youth embeddedness in rural areas. Using a fuzzy cognitive map (FCM), qualitative data have been translated in quantitative, giving evidence on key variables and their inter-relationships that influence young people's decision-making in a GI complex system. A total of twenty-two categorized variables have been identified. Results show how the young entrepreneurs' thinking in GI is structured, based on the cause-effect relationships between the variables. This study finds evidence that the GI system facilitates young generations of farmers in developing a personal approach to modern agribusiness starting from traditions and origins. At the same time, it gives evidence of a new perspective for involving young people in agricultural careers. In this sense, the present research contributes to the literature on factors that add attractiveness to the agricultural sector, to lead researchers and policymakers in dealing with alternative strategies for incentivizing youth involvement in farming.

The shortage of young people in agriculture and the decline of rural areas are increasingly pressing issues that capture the attention of European policymakers and researchers. Despite the low rate of youth involved in agricultural production, recent data reveal a significant proportion of young farmers in Italy that are engaged in geographical indication (GI) production. Statistics provide trend analysis, but they alone are not sufficient in clarifying the motivations behind young people's decision-making. We conducted a qualitative study of Sicilian youth involved in GI to understand their motivations to pursue GI certification and the implications for youth embeddedness in rural areas. Using a fuzzy cognitive map (FCM), qualitative data have been translated in quantitative, giving evidence on key variables and their inter-relationships that influence young people's decision-making in a GI complex system. A total of twenty-two categorized variables have been identified. Results show how the young entrepreneurs' thinking in GI is structured, based on the cause-effect relationships between the variables. This study finds evidence that the GI system facilitates young generations of farmers in developing a personal approach to modern agribusiness starting from traditions and origins. At the same time, it gives evidence of a new perspective for involving young people in agricultural careers. In this sense, the present research contributes to the literature on factors that add attractiveness to the agricultural sector, to lead researchers and policymakers in dealing with alternative strategies for incentivizing youth involvement in farming. [ABSTRACT FROM AUTHOR]

Deep learning models (DLMs), such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU), are superior for sequential data analysis due to their ability to learn complex patterns. This paper proposes enhancing performance of these models by applying fuzzy c-means (FCM) clustering on sequential data from a nonlinear plant and the stock market. FCM clustering helps to organize the data into clusters based on similarity, which improves the performance of the models. Thus, the proposed fuzzy c-means recurrent neural network (FCM-RNN), fuzzy c-means long short-term memory (FCM-LSTM), fuzzy c-means bidirectional long short-term memory (FCM-Bi-LSTM), and fuzzy c-means gated recurrent unit (FCM-GRU) models showed enhanced prediction results than RNN, LSTM, Bi-LSTM, and GRU models, respectively. This enhancement is validated using performance metrics such as root-mean-square error and mean absolute error and is further illustrated by scatter plots comparing actual versus predicted values for training, validation, and testing data. The experiment results confirm that integrating FCM clustering with DLMs shows the superiority of the proposed models. [ABSTRACT FROM AUTHOR]

Bisphenol A (BPA) is a vital raw material used to manufacture various household and commercial goods. However, BPA is a contaminant of emerging concern (CEC) and an endocrine-disrupting chemical (EDC) capable of migrating and bio-accumulating in environmental and biological compartments. At threshold levels, they become toxic causing adverse health and environmental issues. BPA's occurrence in food, food contact materials (FCMs), beverages, water, cosmetics, consumer goods, soil, sediments, and human/biological fluids across Africa was outlined. Unlike most reviews, it further collated data on BPA remediation techniques, including the human and ecological risk assessment studies conducted across Africa. A systematic scrutiny of the major indexing databases was employed extracting relevant data for this study. Results reveal that only 10 out of 54 countries have researched BPA in Africa. BPA levels in water were the most investigated, whereas levels in cosmetics and consumer goods were the least studied. Maximum BPA concentrations found in Africa were 3,590,000 ng/g (cosmetic and consumer goods), 154,820,000 ng/g (soils), 189 ng/mL (water), 1139 ng/g (food), and 208.55 ng/mL (biological fluids). The optimum percentage removal/degradation of BPA was within 70–100%. The potential health and ecological risk levels were assessed by comparing them with recommended limits and were found to fall within safe/low risks to unsafe/high risks. In conclusion, this study revealed that there is still little research on BPA in Africa. Levels detected in some matrices call for increased research, stricter health and environmental regulations, and surveillance. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research explores the application of neutrosophy to understand and address the inherent complexity in the oral transmission of knowledge in family agriculture. It leverages its ability to handle contradictions and ambiguities, facilitating a richer analysis of agricultural practices and social interactions. Orality, as the primary form of communication in these communities, is essential for the transmission of agricultural and cultural knowledge. This research highlights how orality acts as a vehicle for the preservation of collective identity and the transmission of ancestral knowledge. The study used Fuzzy Cognitive Maps (FCM) to describe the causal relationships between different variables of interest. Fieldwork was conducted between October 2023 and January 2024, using semi-structured interviews to capture the oral narrative of the farmers in Apagua. The analysis of the FCM showed that the components of cultural preservation and knowledge transmission are the most influential. This study demonstrated that neutrosophy is an effective tool for analyzing the complex dynamics of oral knowledge transmission in family agriculture. The conclusions underline the interdependence between knowledge transmission, social relationships, adaptation to changes, and cultural preservation. [ABSTRACT FROM AUTHOR]

Geophysical methods depend on a range of physical and chemical mechanisms, and each method has different sensitivities and resolution of Earth parameters, and over different scale lengths. Additionally, such geophysical methods will also have their own statistical characteristics. Thus, it is a significant challenge to combine different methods through a single inversion framework. Rather than attempting to find an optimal and single model for different geophysical responses, an alternative approach is to use FCM clustering to identify clusters of parameters from two or more different geophysical data sets or models that have similar statistical properties. In this paper, we apply the FCM approach to integrate data and model sets for an array of 100 broadband magnetotelluric (MT) and 100 passive seismic receivers spaced 1 km apart on a 10 by 10 grid above the Vulcan IOCG prospect in the Olympic Cu–Au Province, southern Australia. The challenge for exploration of the Vulcan prospect is that it lies beneath 750 m of regolith sedimentary cover, no single geophysical method provides a unique characterization of the deposit geometry, and drilling is very expensive. Fuzzy c-mean cluster analyses are undertaken in 2D for gravity data and shear-wave velocity model data for basement depths beneath cover and in 3D for shear-wave velocity and electrical resistivity model data. Fuzzy c-mean clustering is shown to provide a simple and efficient method of integrating different geophysical measurements to produce a geological framework that can be verified with drilling information. [ABSTRACT FROM AUTHOR]

The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hybrid convolutional-SVM-fuzzy C-means model combining convolutional neural network (CNN) with the classifier support vector machine (SVM) and clustering technique fuzzy C-means (FCM). The proposed model was implemented on Br35H, BraTs 2020 and BraTs2021 datasets. The suggested model outperformed the existing methods by achieving 98.6% of accuracy on Br35H dataset and dice score of 0.63, 0.87, 0.81 on BraTs 2020 dataset for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively. The achieved dice scores on the BraTs 2021datasets are 0.89, 0.95, and 0.89 for ET, WT, and TC, respectively. The results show that the suggested model HybriCSF outperforms the other CNN-based models in terms of accuracy. [ABSTRACT FROM AUTHOR]

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries. [ABSTRACT FROM AUTHOR]

In this paper an improved multiple adaptive neuro-fuzzy inference system (MANFIS) to solve the wheeled mobile robots path planning in unstructured environments is proposed. The fuzzy C-means (FCM) clustering method is used in ANFIS to decrease the input data size, which leads to predict the efficiency of the proposed robot path planning model. The FCM clustering method allow classifying the robot sensors extracted input data into clusters; each cluster has similar properties that assists to develop the correlation between data and as a result simplify the proposed model. The design MANFIS architecture takes both the advantages of artificial neural network which has self-learning ability and fuzzy system to describe the uncertain phenomena of the data. Finally, by combining cluster data and MANFIS an optimum velocity for left and right wheel of the robot is determined, which safely navigate the robot in an optimized route. The simulation and experimental results show that the proposed path planning method is effective and can be implemented for any complex environments. The results obtained by the simulation and in physical experiments are equated with each other and noticed a good promise between both the results as the difference in results is less than 9%. The simulation results consistently demonstrate the superiority of the proposed FCM-ANFIS, manifesting performance improvements of up to 4.4% for path length reduction and up to 8.9% for mobile robot time duration reduction when compared to heuristic methods. This paper identifies and describes a new development on path planning technique that will help the robots to navigate in any kind of uncertain navigation environments. [ABSTRACT FROM AUTHOR]

Agriculture is essential to any country's economy. Agriculture is crucial not only for feeding a country's population but also for its impact on other businesses. The paradox of agri-food companies generating substantial profits despite seemingly high product prices is explored in this article, focusing on the role of digital marketing within the agri-food industry. Enhanced digital marketing performance leads to efficient advertising campaigns, through reduced advertising costs and increased resource efficiency. To do so, the authors collected web analytical data from five established agri-food firms with the highest market capitalization. Then, linear regression and correlation analyses were used, followed by the utilization of fuzzy cognitive mapping (FCM) modeling. The analysis revealed that increased traffic through search sources is associated with reduced advertising costs. Additionally, enhanced website engagement contributes to lower advertising expenses, emphasizing the optimization of the user experience. However, it has been discovered that allocating funds for social media advertising eventually results in higher expenses with higher website-abandoning rate. Ultimately, successful management of the balance between product costs and profitability in the agri-food sector lies on the increased use of search sources and greatly reducing the use of social media sources. [ABSTRACT FROM AUTHOR]

Restaurant reviews play a pivotal role in shaping consumer decisions and perceptions. Analyzing these reviews through sentiment analysis provides valuable insights into customer sentiments towards various aspects of dining experiences, such as food quality, service, ambiance, and pricing. By leveraging sentiment analysis techniques, businesses can better understand customer preferences, identify areas for improvement, and enhance overall customer satisfaction. This research focuses on utilizing aspect-based sentiment analysis to predict restaurant survival, leveraging customer-generated content from online reviews. The proposed methodology encompasses data acquisition, pre-processing, feature extraction, and unsupervised approaches-based classification. Data pre-processing involves tokenization, stop word removal, lemmatization, punctuation removal, and filtering short and long words to standardize the format. Feature extraction includes lexicon-based and word encoding methods, leveraging Term Frequency-Inverse Document Frequency (TF-IDF) vectors, Ngram, Bag of Words, and Word Embedding. Unsupervised approachesbased classification entails Fuzzy C-Means (FCM), K-Means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Hierarchical Method, Hybrid Binary Particle Swarm-Optimized FCM, and HBPSO-Optimized Kmeans. Evaluation parameters are defined to assess the performance of each approach. The results showcase the effectiveness of aspect-based sentiment analysis in predicting restaurant survival, with HBPSO-Optimized FCM demonstrating the highest accuracy at 89.50%. These findings underscore the significance of leveraging customergenerated content for informed decision-making in the restaurant industry. [ABSTRACT FROM AUTHOR]

This study aimed to determine the acute effects of the French contrast method (FCM) and post-activation potentiation (PAP) protocols on 3 × 3 basketball game demands and thermal asymmetry in male basketball players and to compare these effects between protocols. Eighteen male basketball players (mean ± SD; age: 21.7 ± 1.5 years, 10.6 ± 1.9 years of experience) visited the laboratory four times, 72 h apart. The players participated in three different protocols (baseline: 3 × 3 game; FCM + 3 × 3 game; PAP + 3 × 3 game; respectively). The players’ internal and external loads were monitored, game profiles were analyzed, and thermography was applied during the protocols. The results revealed that FCM and PAP did not significantly differ in internal load; however, the significant highest total distance and distance in band 2 during the 3 × 3 basketball game was after the FCM. The 1-point attempt was significantly higher after the FCM, and turnover was significantly higher after PAP. Significant thermal asymmetry was observed in the abdominals and lower back after the FCM and PAP. The results of this study provide coaches and practitioners with detailed information regarding the game demands that can be used to improve the playing profile of 3 × 3 basketball players.

In the past few years, there have been many studies addressing the simulation of COVID-19's spatial transmission model of infectious disease in time. However, very few studies have focused on the effect of the epidemic environment variables in which an individual lives on the individual's behavioral logic leading to changes in the overall epidemic transmission trend at larger scales. In this study, we applied Fuzzy Cognitive Maps (FCMs) to modeling individual behavioral logistics, combined with Agent-Based Modeling (ABM) to perform "Susceptible—Exposed—Infectious—Removed" (SEIR) simulation of the independent individual behavior affecting the overall trend change. Our objective was to simulate the spatiotemporal spread of diseases using the Bengaluru Urban District, India as a case study. The results show that the simulation results are highly consistent with the observed reality, in terms of trends, with a Root Mean Square Error (RMSE) value of 0.39. Notably, our approach reveals a subtle link between individual motivation and infection-recovery dynamics, highlighting how individual behavior can significantly impact broader patterns of transmission. These insights have potential implications for epidemiologic strategies and public health interventions, providing data-driven insights into behavioral impacts on epidemic spread. By integrating behavioral modeling with epidemic simulation, our study underscores the importance of considering individual and collective behavior in designing sustainable public health policies and interventions. [ABSTRACT FROM AUTHOR]

In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper, through clustering techniques like fuzzy c-means (FCM), possibilistic fuzzy c-means (PFCM), and possibilistic c means (PCM) and sample entropy features are extracted from normal and cirrhotic liver ultrasonic images. The extracted features are classified as normal and cirrhotic through the Gaussian mixture model (GMM), Softmax discriminant classifier (SDC), harmonic search algorithm (HSA), SVM (linear), SVM (RBF), SVM (polynomial), artificial algae optimization (AAO), and hybrid classifier artificial algae optimization (AAO) with Gaussian mixture mode (GMM). The classifiers' performances are compared based on accuracy, F1 Score, MCC, F measure, error rate, and Jaccard metric (JM). The hybrid classifier AAO–GMM, with the PFCM feature, outperforms the other classifiers and attained an accuracy of 99.03% with an MCC of 0.90. [ABSTRACT FROM AUTHOR]

Segmentation of brain Magnetic Resonance Images (MRIs) into various brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) is very important to detect and diagnose different brain-related disorders at the primitive level. Accurate segmentation of brain MRIs is very difficult because of the intricate anatomical structure of the tissues, the existence of Intensity Inhomogeneity (IIH), noise, and Partial Volume Effects (PVE). Clustering-based methods are generally used to segment brain images. This work proposes a Chaotic based Enhanced Firefly Algorithm Integrated with Fuzzy C-Means (CEFAFCM) for the segmentation of brain tissues WM, GM, and CSF from brain MRIs. The proposed method can handle IIH, PVE, and noise. CEFAFCM is a spatially modified FCM algorithm combined with the Firefly Algorithm (FA) along with a chaotic map for the initialization of the population of fireflies. The algorithm is tested with brain MRIs acquired from the BrainWeb database. The experimental results demonstrate that the proposed technique is producing better results in comparison with some existing brain MRI segmentation methods such as FCM, BCFCM, FAFCM, and En-FAFCM. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cancer is an acute disease that kills many people around the world, so early detection is a vital need. This study aims to investigate the effectiveness of techniques used in detecting, isolating and extracting tumors in PET/CT images using clustering techniques: K-means, Fuzzy C-mean, and hybrid technique. The results showed that the applied methods were sufficient to detect, isolate and extract areas of the tumor. The calculated tumor area was compared with the nuclear medicine specialist demarcation area, and the percent relative difference ranged between 0.135%-4.86%. As well as the results indicated that implementing the hybrid technique reduced the elapsed time required, and the reduction percentage ranged between 43.03%-97.45%. [ABSTRACT FROM AUTHOR]

The rapid development of wireless technology has led to the availability of a wide range of networked devices that support numerous applications. Small wireless devices that are powered by batteries create a Wireless Sensor Network (WSN), which collaborates to communicate data through wireless channels to a Base Station (BS). However, a WSN system faces a number of difficulties, with energy efficiency being the most critical one. In order to provide energy efficiency and increase network lifespan, it is crucial to lessen the energy required for data transmission. This research suggests an energy-efficient optimal cluster-based routing strategy to extend the lifespan of a network. Energy conservation is of paramount importance in WSNs featuring mobile nodes. Numerous routing techniques have been proposed to reduce packet loss and boost energy efficiency in such networks. These protocols are not particularly energy-efficient though, because they cannot build the right clusters. In this paper, the tree-based Hybrid Fuzzy C-Means Genetic Algorithm (HFCM-GA) is presented in an attempt to reduce energy loss and increase the packet delivery ratio. Using node mobility and the node energy attribute, this protocol proposes a centralized cluster creation mechanism that produces optimal clusters. Node mobility, node energy, and node distance are additional criteria that a detached node considers while choosing its ideal cluster head. Simulation outcomes demonstrate that the recommended HFCM-GA is superior to the conventional routing protocols regarding the residual energy and coverage ratio. [ABSTRACT FROM AUTHOR]

To improve the accuracy of terminal area meteorological scene pattern recognition, this study adopts a clustering model based on Stacked Denoising Autoencoder. Noise is added to the input layer, and a three-layer autoencoder is constructed for greedy layer-wise training. The reduced-dimensional features are used as inputs for clustering to achieve meteorological scene pattern recognition. The method is validated using one year of meteorological data from Tianjin Binhai International Airport. Traditional similarity distance measures such as Euclidean distance, Hamming distance, and Manhattan distance are used with both K-medoids and FCM clustering methods. The results show that the similarity measure based on SDAE performs the best in both K-medoids and FCM clustering, with a difference rate of 22.4%, 12%, 17.7%, and 24.8%, 10.7%, 11.8% compared to other similarity measures, respectively. It also has the shortest computation time, demonstrating that the SDAE-based measure and clustering achieve the best performance. Ultimately, eight meteorological scenes are identified with clear and distinct classifications. [ABSTRACT FROM AUTHOR]

Paper presents an analysis of the effects of high-pressure N2 and enriched N2 injection on oil recovery under multicontact miscible and first contact miscible conditions. Phase equilibrium experiments, slimtube, and core displacement tests were carried out at the reservoir conditions to study miscible N2 flooding. Utilizing the phase equilibrium and PVT data, pseudoternary diagrams were constructed to determine the minimum enrichment required for FCM displacement. It emerged that methane vaporization is one of the main characteristics of N2 flooding, and this characteristic amplifies clearly as more N2 dissolves in the crude oil. Moreover, the coreflood results indicated that the oil recovery by enriched N2 injection is higher than that by N2 injection at the early stage of production. This was probably due to the fact that there is no interfacial tension between the enriched N2 and oil when they contact each other under FCM displacement. [ABSTRACT FROM AUTHOR]

The frequent occurrence of extreme weather makes people pay more attention to environmental protection. To cope with the global climate problem, various countries re-plan social development through the concept of low-carbon. As greatly popularized by the Internet, the topic of low carbon concept is spread more through online social media, so it is urgent to understand the user’s attention to low carbon topics in a more intelligent way for subsequent relevant publicity and policy guidance. This paper studies the low-carbon topic of attention in the context of social media. First, the BERT (Bidirectional Encoder Representation from Transformers) model is used to complete the word vector feature extraction of acquired data; Secondly, the FCM method was used to complete the clustering analysis of the main topics in the low-carbon concept, and the PSO method was used to optimize the model. After optimization, the accuracy of clustering for various topics was higher than 80%. For the Esse index of cluster center variance, the method proposed in this article is also close to 10% due to other classic methods; Finally, this paper carried out an application test of low-carbon topics in the region, achieved good results, and made a detailed analysis of the distribution of various topics. It can be predicted that this method will provide more public opinion references for low-carbon development paths in various countries and regions in the future, and provide technical support for information dissemination and analysis under social media.

Push notifications are widely used in Android apps to show users timely and potentially sensitive information outside the apps’ regular user interface. Google’s default service for sending push notifications, Firebase Cloud Messaging (FCM), provides only transport layer security and does not offer app developers message protection schemes to prevent access or detect modifications by the push notification service provider or other intermediate systems. We present and discuss an in-depth mixed-methods study of push notification message security and privacy in Android apps. We statically analyze a representative set of 100,000 up-to-date and popular Android apps from Google Play to get an overview of push notification usage in the wild. In an in-depth follow-up analysis of 60 apps, we gain detailed insights into the leaked content and what some developers do to protect the messages. We find that (a) about half of the analyzed apps use push notifications, (b) about half of the in-depth analyzed messaging apps do not protect their push notifications, allowing access to sensitive data that jeopardizes users’ security and privacy and (c) the means of protection lack a standardized approach, manifesting in various developer-defined encryption schemes, custom protocols, or out-of-band communication methods. Our research highlights gaps in developer-centric security regarding appropriate technologies and supporting measures that researchers and platform providers should address.

With the rapid development of information technology and the advancement of medical informatization, medical big data plays an increasingly important role in diagnosis, treatment, health management, and other aspects. However, the high sensitivity and privacy of medical data also bring serious security challenges. A privacy risk assessment model combining information entropy and fuzzy C-means clustering algorithm is proposed to address this issue. This model is based on information entropy to construct an access control model and quantify the privacy risks of user access behavior. Cluster analysis is conducted on users using the fuzzy C-means clustering algorithm, and different permissions are assigned based on their access habits. The experimental results show that when the iteration number is 120, the root mean square error value of the improved fuzzy C-means clustering model is 0.08, and the accuracy is 0.98. When the dataset is 100, it can be seen that each model can learn the information in the dataset relatively completely. When the dataset reaches 800, the judgment time of the improved fuzzy C-means clustering model is 0.6 seconds. When the number of users reaches 100, the judgment time of the improved fuzzy C-means clustering model is 1.8 seconds. The research results indicate that the proposed medical big data privacy risk assessment model, which combines information entropy and improved fuzzy C-means clustering algorithm, has excellent performance and can provide new technical means for medical data privacy protection, enhancing the security and reliability of medical information systems.

The rapid advancement of technology and increased internet usage have significantly contributed to the development of multimedia technologies, such as computer games and computer graphics. A key aspect of these advancements is the creation and widespread use of 3D models. However, this rapid development has also led to issues like illegal usage and theft of 3D models. Ensuring ownership protection is crucial to addressing these challenges. An effective method for copyright protection and preserving the integrity of 3D mesh models is digital watermarking. 3D watermarking is essential for safeguarding the copyright of 3D mesh models. This proposed study introduces an innovative approach that combines three powerful techniques. Fuzzy C-Means (FCM), Gaussian Mixture Model (GMM), and K-Means clustering to cluster similar vertices and embed watermarks. These techniques are chosen due to their simplicity, efficiency and accuracy that are proven in other domains. In the proposed work the cluster size is varied for each algorithm, and the watermark is embedded in the largest cluster, specifically in the ‘Z’ coordinate of the model. To test the robustness of the watermark, common attacks and geometric attacks are applied to the watermarked models. The embedded watermark is extracted with an NCC value close to 1 for all clustering algorithms, indicating high accuracy. The study evaluates the imperceptibility and robustness of the watermarking method, achieving high Peak Signal To Noise Ratio (PSNR) values and low Root Mean Square Error (RMSE), which demonstrate the effectiveness of the novel approach. The results indicate that the method attains excellent Normalized Correlation Coefficient (NCC) values, confirming its reliability and robustness.

A converter valve is the core equipment of HVDC transmission system, whose operating temperature threshold is strictly constrained. This paper proposes a novel prediction method for outlet water temperature of converter valve based on F-BP network, which aims to accurately predict the outlet water temperature and assists the operation and maintenance personnel to take measures in time so that the temperature of the converter valve will not exceed its preset threshold when the operation condition has changed. Firstly, the principle and method of the construction of typical operation databases of converter valve is stated, including data standardization, the calculation of the optimal clustering category number and the final clustering process. Then, the steps of using the typical operation databases and BP neural network to make predictions are presented. Using MATLAB, we predicted the outlet water temperature of a converter valve in Chuxiong Converter Station with F-BP method and two other existing methods in comparison. The results indicate that the proposed approach’s prediction accuracy increases by 0.9141 °C and 0.9938 °C respectively compared with the simple BP neural network and linear regression, which contributes to the prediction application of the outlet water of a converter valve.

Medical image segmentation is a critical task in medical image analysis, and clustering algorithms can be utilized to achieve this goal. This research work focuses on the segmentation of neuro disorder magnetic resonance images using Otsu, K-means, and FCM coupled with the firefly optimization algorithm. Otsu is a classical thresholding algorithm that relies on a single threshold value to segment the images. K-Means is one of the simplest and most widely used clustering algorithms. It aims to partition data into K clusters, where each data point belongs to the cluster with the nearest mean. Fuzzy C-Means is an extension of K-Means, allowing data points to belong to multiple clusters with varying degrees of membership. In medical image segmentation, FCM was used to classify pixels or voxels into different tissue classes with soft boundaries, accounting for partial volume effects. Firefly Optimization helps in improving the convergence speed of the FCM algorithm. Firefly optimization is good at exploring the solution space and finding global optima. The combination of FCM and Firefly Optimization leads to more accurate clustering results. The performance evaluation was done by Renyi entropy and Shannon entropy, FCM coupled with the firefly optimization was found to exhibit superior results when compared with the Otsu and K-means clustering algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Segmentation of Lung Region is an important step in the clinical diagnosis of lung diseases like Pneumonia, Tuberculosis, and COVID-19. However, most the methods employed Deep learning algorithms without providing any additional information at the segmentation phase. Hence this paper proposes a new segmentation Mechanism based on the Fuzzy C-Means (FCM) and different features. A new Variant of FCM called as Dual FCM is introduced which forms an objective function based on two different Sub-Objective Functions (SOFs). The first SOF segments the images through gray pixel intestines while the second SOF used features for segmentation. Two different features are derived namely morphological features and gradient features from every CXR image. Finally, the segmented results at individual SOFs are fused to get a final segmented result. Extensive experiments have been carried out over the proposed approach through two publicly available datasets namely Montgomery County (MC) Dataset and Shenzhen Dataset. The performance is assessed through two evaluation measures namely Jaccard Index (JI) and Dice Coefficient (DC) and the average obtained is noticed as 92.985% and 95.295% respectively. Further the proposed segmentation mechanism is compared with several past lung image segmentation approaches. The approximate improvement in the JI and DC from past methods is noticed as 4.2210% and 2.3150% respectively. [ABSTRACT FROM AUTHOR]