The global burden of cardiovascular diseases (CVD) is on the rise. Central arteriosclerosis, a critical pathological mechanism underlying CVD, is influenced by multiple factors. Given the limitations of current single-target pharmacological interventions, there is an urgent need to investigate multi-target therapeutic strategies. This study employs a network pharmacology approach, integrating data from the SwissTargetPrediction and GeneCards databases toidentify 37 intersection targets of isoflavones and central arteriosclerosis. Aprotein-protein interaction (PPI) network is constructed to pinpoint core targets, including SRC, CASP3, ESR1, EGFR, and STAT3. Gene Ontology (GO) andKyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicate that isoflavones modulate inflammatory responses, apoptosis, andoxidative stress by regulating the "Cytokine-cytokine receptor interaction" pathway. The multi-target synergistic effects of isoflavones emulate the activityof the estrogen receptor (ESR1), inhibiting endothelial cell apoptosis and suppressing STAT3-mediated inflammatory signal transduction, thereby enhancing vascular homeostasis. In comparison to traditional statins, the multi-target mechanism of isoflavones aligns more closely with the intricate pathological network of arteriosclerosis, offering novel insights for the development of natural therapeutics. This study elucidates the comprehensive therapeutic potential of isoflavones. Future experiments are necessary to validate the specific targets and dose-response effects to facilitate clinical applications.

Background With the aging population in the Western Pacific region, the rising burden of liver cancer associated with high body mass index (BMI) has become a focus of public health concern. This study aims to assess trends in liver cancer burden linked to high BMI among the elderly (60+) from 1990 to 2021 and explore its relationships with aging, socioeconomic development, and human resources for health (HRH). Methods Data on deaths and disability-adjusted life years were sourced from the Global Burden of Disease Study (GBD) 2021, while HRH data came from GBD 2019. The estimated annual percentage change was calculated to assess trends, and decomposition analysis examined the contributing factors. Spearman’s rank correlation was used to evaluate relationships between disease burden and the Socio-demographic Index (SDI) and HRH. Results From 1990 to 2021, the liver cancer burden linked to high BMI significantly increased among the elderly in the Western Pacific. China had the highest overall burden due to its large population, while Australia saw the greatest growth. Decomposition analysis revealed that population growth and epidemiological changes were the key drivers. However, the relationship between disease burden and SDI, as well as HRH density, was weak. Conclusion The burden of liver cancer caused by high BMI in the Western Pacific region remains a serious public health issue. Promoting the early diagnosis and comprehensive intervention of obesity and its related diseases, optimizing the allocation of health resources, and strengthening the management of chronic diseases are key strategies for reducing the burden of liver cancer.

Background Internet addiction has become a growing concern among adolescents, particularly in China, where the pervasive use of digital technology can interfere with daily life and academic performance. Previous research has highlighted various factors contributing to internet addiction, including physical activity and social interaction. However, the relationship between these factors and internet addiction remains under explored, especially in the context of Chinese middle school students. Methods This study was conducted in November 2024 among 2336 middle school students from five secondary schools in Guangdong Province, China. A self-reported questionnaire was distributed to measure students’ demographic information, physical activity levels, social interaction, and internet addiction. Physical activity was assessed using the International Physical Activity Questionnaire(IPAQ) short form, and internet addiction was measured using the Chinese Internet Addiction Scale(CIAS-R). Regression analysis was performed to examine the relationships between physical activity, social interaction, and internet addiction. Results The findings revealed that physical activity was initially a significant predictor of internet addiction, but its effect diminished when social interaction was included in the model. Social interaction emerged as a strong predictor of internet addiction, explaining a large portion of the variance. Furthermore, a significant interaction effect was found between physical activity and social interaction, with both factors contributing jointly to the prediction of internet addiction. The final model (R^2 = 0.651) explained 65.1% of the variance in internet addiction, with social interaction(β= 0.107, p < 0.01) and the interaction term (β = 0.071, p < 0.05) being statistically significant. Conclusion This study underscores the importance of both physical activity and social interaction in predicting internet addiction among Chinese middle school students. While physical activity alone had a limited effect, the interaction between physical activity and social interaction was a significant predictor of internet addiction. These findings suggest that interventions targeting both physical activity and social engagement may be effective in mitigating the risk of internet addiction among adolescents. Future research should explore the causal relationships and additional factors influencing internet addiction.

Abstract:\n\n\n Generally, a very critical kind of brain cancer has been defined as Glia Blastemal Multiform (GBM) and is a highly less median survival. So, researches have much attention to classify various glioma cancer cell subtypes in order to envisage the patient’s diagnosis. But, due to various individual basis the survival period of various subtypes of GBM is frequently various. In gene testing, classic subtypes rules has been evolved based on single bimolecular features. This type of classification attained better performance compared than traditional rules, but still the massive data has not been covered in GBM diagnosis prediction. So, here, an Improved Extreme Learning Machine (IELM) with sparse representation based classification combined diagnosis model based on more than one data type could accomplish better in GBM and it will used additional to clinical treatment. In this work, the features are selected based on the Linear Discriminant Analysis (LDA). Then, the long and short term survivors are classified by using IELM with sparse representation. In this IELM, the effectual features are selected by using sparse representation to increase the prediction diagnosis accuracy. This system is evaluated by using the Cancer Genome Atlas (TCGA) database. Simulation outcomes demonstrates that the anticipated IELM acquires superior performance with respect to accuracy rate of 92.35%, sensitivity rate of 93.35%, specificity rate of 92.35%, precision rate of 76.77%, recall rate of 60.25% and f-measure rate of 69.25%compared than exiting Minimum Redundancy — Maximum Relevance (MRMR) with Multiple Kernel Machine (MKL) learning method and Support Vector Machine (SVM).

In the modern world, the digital signal processing embeds more in real time applications. Several researchers focused on filtering process to identify the limitation in traditional methods. In this article, the meta-heuristic algorithm is deployed for optimizing the design of infinite impulse response (IIR) filter. The traditional IIR filter results create computational complexity and its performance is worse in the case of a noisy environment. In signal processing, IIR plays several roles in filtering and monitoring the signal amplitude. The African Buffalo Optimization (ABO) is quite easy for implementation and its performance outcomes solved many problems in various domains. Hence, it is selected for solving the IIR filter problems to obtain the optimal filter coefficients. Initially, the IIR filter is designed for different orders under ABO concept. The performance of the ABO based IIR filter is superior to those obtained by Genetic Algorithm and the cuckoo search algorithm. The performance result of the proposed method proves that it has a smaller magnitude error and phase error with fast convergence rate.

Abstract: Nowadays, breast cancer is one of the major issues among women in developed nations. It occurs based on their lifestyles and eating habits. As per many researchers, cancer leads to death. Likewise, breast cancer is also one of the leading dangerous cancer types that may result in critical issues over a body. Hence to avoid such activities it is necessary to detect the cancer spot and provide some clinical experiments, it is termed as early detection. The detection of this type of cancer is difficult at initial stage because the cancerous tumors are rooted in the common breast tissue structures. The main objective of this research is to model a breast cancer prediction system with a novel machine learning approach based on wavelets is proposed. The prediction of breast cancer for diagnosis process is made by the proposed algorithm named as Optimally Pruned Hybrid Wavelet Kernel-based Extreme Learning Machine (HOP-WKELM). Initially, the input is pre-processed for noise reduction using kuan filter. After that, Quantum Evolutionary Algorithm (QEA) is applied to segment the cancer part in mammogram image and feature extraction using Grey-level co-occurrence matrix (GLCM), Gabor filter and Local Binary Pattern (LBP) features. The extracted features are classified using HWKELM classifier. In this HWKELW, WKELM learning algorithm utilized the Dragonfly Swarm Behavior-based Optimization (DSBO) approach to optimize the parameters of kernel functions. The proposed strategies achieved a maximum accuracy of 98.8% and a maximum precision of 98.1% when compared with existing Adaboost systems.

Abstract: Nowadays, utilization of power system with management and monitoring of smart grid system is essential in global wise. Therefore, Phasor Measurement Units (PMUs) device is used for advanced monitoring of real time system, analysis and control in smart power grid system. An Optimal PMU Placement (OPP) is quite important during planning studies for both existing and future power networks. So, the problems of minimizing the number of PMUs and their optimal location for system are mainly considered. In this work, to achieve better optimal location and reduce the number of PMUsand to increase the measurement redundancy, an Enhanced Bat Algorithm (EBA) is proposed for optimization with phasing module. Phasing is mainly considered for OPP to reduce the cost. In phasing unit, PMUs are separated into three phases and those are concentrated in each phase separately. Finally, IEEE-14 bus standard systems and the Tamil Nadu state power grid (83 bus system) are utilized to test the proposed model. The simulation results are evaluated and compared with existing algorithm to show the efficient process of OPP with better optimization, less cost and minimum redundancy than the existing optimization schemes.

ABSTRACT\nIn the applications of Wireless Sensor Network, sensor nodes broadcast the data towards the destination. Localization is considered as the most important and interesting area in WSN as identifying the location of sensor nodes is needful for many situations. The proposed work, points on discovering the sensor node location and promotes cross layer effective routing path searching for the reliable data transmission in the network. The proposed work consists of three main phases. The initial phase includes the cross layer link establishment. The second phase includes, identifying the fuzziness of wireless sensors. It tracks the location of sensor nodes and analyzes the behavior. This can be done with the algorithm namely Fuzziness based Contiguous nodes Refining Algorithm. Since the characteristics of nodes may vary often, the algorithm finds the unsuitable communication node in the routing path and removes it. Source node controls the activities of the intermediate nodes in the routing path. After identifying and removing the unsuitable nodes, in final phase, an effective routing path is established by using the scheme called Efficient Fuzziness based Contiguous node refining with cross-layer routing Scheme (EFCRS) is proposed. This selects the routing nodes from starting node to ending node with the lesser energy usage. At last, destination node acquires the enhanced throughput and reduced consumption of energy. It also obtains minimum packet latency. Simulation is carried out and found it achieves better performance than existing algorithms in comparing metrics end to end delay, overhead, connectivity ratio, throughput and energy consumption.

Abstract: In this paper a heuristic technique based optimal location and sizing of UPFC is proposed to improve the dynamic stability. The UPFC sizing is predicted and determines the favourable location to fix UPFC with constraint of power flow. Also, it generates the effects of power flow to identify the maximum power loss, voltage, real and reactive power flow. By using GA based Modified Artificial Bees Colony (MABC) algorithm the optimum location has been determined and reduce the power loss with reactive and real power flow. The violated power flow quantity is optimized by using the Improved Gravitational Search Algorithm (IGSA) and the initial condition of UPFC required quantity is recovered. IGSA is used to identify the maximum loss of power to fix the UPFC to avoid power flow affects by generator outage. The proposed work performance analyses are evaluated by implementing the proposed algorithm in the MATLAB/SIMULINK platform. The analysis results of the proposed algorithm shows better performance than the existing and also determine the pre-eminence of the proposed approach and confirm it’s prospective to solve the problem.

The Dede Korkut Epic stories that are a part of the Epics of Oghuz are one of the most important sources that reflect the original culture of the Turkish Nation, that is our own culture.\nFor now there are two samples of these due epics that have been transferred up today. One of them is the Vatikan Sample by Ettori Rossi and the other one the Dresden Sample by Heinrich Friedrich von Diez.\nDiez published two books in 1811 and 1815 he titled as “Denkwürdigkeiten von Asien”. In the first of the books (1811) that of which consists of about 1500 pages, there is a writing that can be translated as “The Book of Oghuz” placed on page 157 of chapter IX of the book “Buch des Oghuz”. The due book consists of 49 pages, in which information is given in the first nine pages and 200 sayings placed on the remaining pages. There are two manuscripts placed in the second book of his that was published in 1815. The first of them is “Buch des Oghuz von Dede Korkut” that can be translated as “Book of the Oghuz by Dede Korkut” and is placed on pages 288-331 of chapter 7. In the same book on pages 399-457 of chapter 14, a writing takes place “Der neuentdeckte oghuzische Cyclop verglichen mit dem Homerischen” that can be translated as “The comparison of the newly found Cyclop of Oghuz with that of Homer”.\nWe will deal upon the third manuscript within the due three manuscripts. There are important identifications in this work translated by us. Diez is writing about the Cyclop in the work of Homer named Odyssee as being inspired by the Cyclop of the Oghuz (Tepegöz of Oghuz). In comparing the Cyclop taking place in the Greek mythology, that is comparing the Greek Cyclop of the Oghuz, this work reveals the fact that the Greek Cyclop was inspired by the Cyclop of the Oghuz and thus comes to the conclusion that it was copied from this original.\nSince it is of utmost significance, the due manuscript is considered to be published by us in two parts. That is; Diez had been able to gather a good amount of knowledge on the subject about the Cyclop. And in our opinion he had translated it truthfully the same. But however in translating the text to German from the language of the Ottoman’s (Osmanlıca) he made some misunderstandings. So as a result, in the first part of the manuscript it will be dealt upon the misunderstandings Diez made while translating and it will be our endevour to reveal the differences between the Cyclop of Homer and the Cyclop of the Oghuz in detail by trying to reflect the point of view of Diez continiuosly.

Abstract: Wireless sensor nodes are fixed in stable environment, so more distance between nodes in network cause packet drop. During transmission period node broadcast data packets there is coverage problem. It affects network lifetime, normal sensor nodes have certain limits for packet exchange, which makes unbalanced load for routing node. It minimizes the packet delivery rate, also consume more energy. Proposed Improved Communication Confining Clustering (ICCC) Method, information is exchanged in cluster member nodes, which are organized by cluster head. Nodes confining with intra clustering, provides the stable link among nodes in cluster achieve effective packet transmission in that node. Unceasing possession Node allocation algorithm is applied to clustering, it merges many feature in cluster members nodes also reduce energy usage range. This method not allows the re-clustering of nodes that reduce time delay.

To study the forklift that is more suitable for handling goods in narrow spaces, the modeling and simulation of four wheel steering (4WS) forklift is studied. Firstly taking the type of TFC35 electric forklift as an example, the principle and the main parts of 4WS system based on steer by wire (SBW) technology are analyzed and researched. Secondly according to the Newton vector mechanics system, a dynamic model of the two degree of freedom (2DOF) is established for 4WS forklift, and then the dynamic model of the three degree of freedom (3DOF) of the forklift is established by Lagrange method to consider the effect of roll motion on the forklift. Finally using MATLAB software to build the simulation models, and under the same simulation parameters, the simulation results show that the two kinds of dynamic models have similar steady-state response, but due to the 3DOF dynamics model considering the roll factor, it can reflect the steering characteristics of actual forklift better than the 2DOF dynamics model. The forklift studied in this paper is flexible and more suitable for working in small space compared with the traditional rear-wheel steering (RWS) forklift.

Abstract\n\n \nIn biodiversity management and conservation, the identification and classification of natural vegetation are considered as a major issue. In this paper, natural vegetation and its formations are identified using a Worldview-2 spectral imagery. The classification of the Worldview-2 image and ancillary thematic data was performed by using an Improved Relevance Vector Machine with Mosquito Flying behaviour based swarm intelligence Optimization (IRVM-MFO) algorithm. Here the perceptive strength of the spectral signature and the Local features of spectral bands are considered in each pixel. In addition texture features such as Fourier spectrum and GLCM features are exploited to make the system more robust. In IRVM, the MFO approach used to optimize the kernel functions of parameters to improve the training process. The proposed IRVM-MFO shows improved classification performance in terms of parameters like overall sensitivity, specificity and accuracy compared with simple RVM and SVM methods. The proposed method has results in a high accuracy of 92.3% and the Kappa index varying between 0.92 and 0.78 at vegetation formation levels.

In an automotive electronics applications there are approximately 230 electronic control units ECU’s are used to provide intelligent driving assistance. So, there is an effective multiple objective real time task scheduling techniques are required to provide better solution in this domain. This paper describes novel multiobjective evolutionary algorithmic techniques such as Multi - Objective Genetic Algorithm (MOGA), Non-dominated Sorting Genetic Algorithm (NSGA) and Multi - Objective Messy Genetic Algorithm (MOMGA) for scheduling real time tasks to a multicore processor based ECU. These techniques improve the performance upon earlier reported of an ECU’s by considering multiple objectives such as, low power consumption (P), maximizing core utilization (U) and minimizing deadline missrate (δ). This work also analysis the schedulability of realtime tasks by computing the converging value of a series of task parameters such as execution time, release time, workload and arrival time. Finally, we investigated the performance parameters such as power consumption (P), deadline missrate (δ), and core utilization for the given architecture. The evaluation results show that the power consumption is reduced to about 5 - 8%, utilization of the core is increased about 10 % to 40% and deadline missrate is comparatively minimized with other scheduling approaches.

The ECG (Electrocardiogram) signal represents electrical behavior of\nheart over time and is measured by placing electrodes on specific locations of\nlimb. These signals are useful for monitoring and diagnosis of heart related issues.\nECG signals are often corrupted by artifacts during acquisition and transmission\npredominantly by high frequency power line interference, electromyography\nnoise and low frequency noise caused by motion of electrodes (baseline changes).\nAddition of these artifacts changes morphology of the ECG signal which affect\naccurate analysis and hence need to be reduced for better clinical evaluation.\nECG signals also generate massive volume of digital data, so they need to be\nsuitably compressed for efficient transmission and storage. Hence, for efficient\ncompression/approximation, in this paper, the ECG signal taken from MITBIH\ndatabase is preprocessed using Total Variation Denoising approach and\nthe preprocessed signals are then characterized using Bottom-Up approach.\nThe individual sections are then approximated using Chebyshev polynomials\nof suitable order. The performance of the approximation technique is measured\nby computing the Maximum Absolute Error, the Compression Ratio, Root\nMean Square Error, Percent Root Mean Square Difference and Percent Root\nMean Square Difference Normalized. The results are also compared with other\ntechniques reported in the literature, where significant improvements in all the\nperformance metrics are observed by the proposed method.

Abstract\n\n As VLSI device is concern low power operation of CMOS devices architecture are significant one. Here in the proposed work, image sensor based on the concept of Pulse width modulation are designed with the operation voltage of less than 300 mV and to operate in low signal to noise ratio. CMOS based image sensor are modelled for the wireless sensors in security purposes. On comparing to the previous sensors, transducer work this proposed work gives the high gain, reduced area structure, good throughput. Here, designed a circuit were CMOS image sensor operates in 0.4V with the output of 0.38V as output voltage, 54dB of dynamic range in sensor pixels.

With the rapid increase in internet users and customer reviews playing the major role in social media gave rise to sentiment analysis. Pre-processing of input text during sentiment analysis eliminates the incomplete and noisy data. Typically, sentiment is manifested separately and applying a pre-processing model for optimizing the cross-domain sentiment classification is highly required. In this paper, a method called Hidden Markova Continual Progression Cosine Similar (HM-CPCS) is proposed to explore the impact of pre-processing and optimize sentiment analysis. First, a measure of subsequent and antecedent probabilities of tags is made using Hidden Markova POS Tagger for the given input dataset. Subsequent and antecedent probabilities of tags are obtained by measuring the transition probabilities between states (i.e. domains) and observations (i.e. review statements) ensuring feature extraction accuracy. Next, the Continual Progression Stemmer continuously stems the text by adding prefix and suffix to form structured words for the given shortcuts and therefore reduce Error Rate Relative to Truncation (ERRT). Finally a Cosine Similarity function is applied to remove stop word for cross-domain sentiment analysis and classification. The performance evaluation of HM-CPCS method is done with standard benchmark data sets of consumer product and services reviews extracted from Sentiwordnet. The parameters used in evaluation are number of customer review words, execution time, accuracy and error rate. Experimental analysis shows that HM-CPCS method is able to reduce the time to extract the opinions from reviewers by 46% and improve the accuracy by 9% compared to the state-of-the-art works.