Several frequent stable point theorems are formulated using perfect intuitionistic fuzzy metric (IFM) spaces and organized in this article. The purpose of this article is to validate these theorems under conditions that increase their future applicability. The concept of reciprocally continuous mappings functions as a requirement for two functions to stay mutually continuo us together while weakly compatible mappings need supplementary conditions to determine fixed points. The findings achieve better strength because the methodology incorporates associated sequences. The study contributes to intuitionistic fuzzy metric (IFM) spaces fixed point theory by implementing various mathematical methods. The paper has specific aims to advance current findings through an exchange of complete metric spaces with complete IFM spaces.

The radiology unit plays a vital role in recent healthcare, aiding as a significant tool for detecting disease/disorders, observing their evolution, and facilitating treatment through numerous imaging procedures. This paper had an attempt to review of the foremost application of AI in the field of radiology, and to consider possible future evolution. This review article synthesizes outcomes from foremost scientific sources like PubMed, Google Scholar, and Science Direct, Springer etc. dazzling the ongoing evolution and prospects and barriers of AI in radiology and its subdivisions. This review article finds that AI assists as a authoritative optimization tool, helping technicians and radiologists to choose custom-made patient protocols, monitor dose constraints, and speculate radiation risks. It also augments the reporting workflow by associating words, imageries, and quantifiable data flawlessly. Hence, the role of AI in radiology sector have developed remarkable in the preceding decade. However, AI in radiology aspects challenges associated to data testing and validation of data, professional acceptance, as well as education and professional training. Despite the challenges, AI affords a prospect for superior novelty in the radiology field with amended accuracy, condensed burden of radiologists and improved patient concern.

The problems that women and children encounter in their daily lives are more numerous. Basically, there are several problems while going outdoors and coming back, including kidnapping. The most pressing issue facing our nation is the prevalence of crime against women, which has an impact on socioeconomic status, women\'s development, and public safety. According to the crucial scenario, the primary cause of the high crime rate scenario is brutality towards women. The government works to improve the quality of life for citizens and decrease crime in order to combat the issue against women. Even with the development of applications and other forms of contact information, the rate of crime against women continues to rise. This issue is considered to be a major one in our day and age: Eventually, the inference of crime rates at the stranger level is raised. Traditional methods for calculating the crime rates in a certain location. The battery sustainability algorithm used in the women\'s tracking system has been adjusted to use less power and manage battery power. Compared to the current DSR protocol agent, the suggested method greatly lowers consumption of power overhead and enhances message transmission between source and destination.

In vitro fertilization (IVF) is an important assisted reproductive technology (ART) that relies on accurate embryo assessment and follicle tracking to improve success rates. Recent advances in deep learning and 3D visualization have provided promising solutions to automate and improve both embryo and follicle assessment. This study proposes a deep learning framework for IVF that includes pre-processing, segmentation, and classification techniques. Pre-processing includes non-local means (NLM) filtering and normalization to reduce noise while preserving important morphological details in 3D embryo and follicle imaging. This step ensures improved contrast and clarity, which enables better downstream processing. For segmentation, a U-Net-based framework is used to precisely define reproductive structures such as oocytes, embryos, and follicles, which facilitates accurate localization and feature extraction. Segmentation plays a key role in identifying regions of interest and aiding subsequent classification. By focusing on the segmented regions, the R-CNN model distinguishes between viable and non-viable embryos, as well as follicle maturity stages, and automates the grading process with high accuracy. In this approach used as two datasets as 3D ultrasound images and 3D OCT images. The proposed 3D deep learning approach provides an automated, objective, and efficient method for embryo and follicle assessment, which reduces the subjectivity of manual assessment. This study highlights the importance of deep learning-based 3D vision techniques in revolutionizing IVF procedures and advancing reproductive medicine.

The accurate prediction of chronic heart disease represents a critical need for conducting appropriate interventional actions and individual patient care. The proposed research presents a supervisory machine learning framework combining cutting edge feature engineering with hyperparameter optimization to boost prediction capabilities. Mutual Information and Lasso Regression techniques revealed age and ap_hi and cholesterol as the main predictors which were chosen for analysis. Introducing Genetic Algorithm based hyperparameter optimization led Logistic Regression models to achieve an AUC of 0.95 starting from 0.79. The AUC scores of Decision Tree and Random Forest models increased to 0.86 from 0.83 and to 0.80 from 0.77. The performance evaluation metrics for MCC reached near 0.90 for both Logistic Regression and Decision Tree. The described techniques demonstrated their capability to enhance machine learning models for heart disease prediction thus enabling more precise risk assessment for at risk patients.

Researchers examined how machine learning algorithms predict chronic heart disease (CHD) based on a dataset which included 70,000 patient records. The evaluation took place following an assessment of three models namel y Logistic Regression, Decision Tree and Neural Network through multiple performance metrics that included accuracy, precision, recall, F1 score, AUC ROC and Matthews Correlation Coefficient (MCC). The Logistic Regression model demonstrated superior perfor mance with 91% accuracy along with an AUC of 0.78 and earned the best three metrics of precision and recall and F1 score. The accuracy with Decision Tree reached 85% but it performed poorly in MCC (0.70) and AUC was at 0.64. The Neural Network achieved 88% accuracy yet produced the least AUC score at 0.55 while its MCC reached 0.75. The prediction results verify that Logistic Regression stands as the best model choice for CHD diagnosis among the tested models.

Diabetes is a chronic metabolic illness marked by high blood glucose\nlevels, which can lead\nto serious complications if not recognized and treated promptly. Diabetes must be predicted\naccurately and early to allow for appropriate intervention and treatment planning. This study\nseeks to create a high performance predictive model for diabetes diagnosis by combining\nadvanced pre-processing, feature selection, and classification techniques. A unique hybrid\nstrategy is proposed, which uses Dynamic Incremental Normalization to effectively\nnormalize the data distribution and the Evolutionary Gravitational Search Algorithm to\ndiscover the most significant features, improving model accuracy and efficiency. A hybrid\nRecurrent Neural Network (RNN) and Radial Basis Function Network (RBFN) model is used\nfor classification, taking use of RN N\'s capacity to capture sequential dependencies and\nRBFN\'s ability to handle nonlinear decision boundaries. The model was tested with the PIMA\nIndian Diabetes Dataset and 10 fold cross validation, yielding an accuracy of 99.5%,\nprecision of 98.9%, recall o f 99.1%, and F1 score of 99.3%.

Freezing of Gait (FoG) is a debilitating symptom of Parkinson’s Disease (PD) that severely impacts mobility and quality of life. Early prediction of FoG can facilitate timely interventions, improving patient outcomes. This study proposes a multimodal sensor fusion approach combined with deep learning and Internet of Things (IoT) technology for real-time FoG prediction. The system integrates data from inertial measurement units (IMU), electromyography (EMG), electroencephalography (EEG), and foot pressure sensors. Pre-processing includes Noise removing window design method using a sliding window approach. Feature extraction in time, frequency, and nonlinear domains is followed by feature selection using Linear Discriminant Analysis (LDA) and Mutual Information (MI) a hybrid CNN-LSTM deep learning model is employed to capture spatial and temporal patterns in gait data. This approach paves the way for intelligent, real-time wearable monitoring systems, enhancing mobility, safety, and quality of life for PD patients.

This research describes a seven-level RSS-MLI, a reduced switch symmetrical multilevel inverter employs MPPT or maximum power point tracking, is determined by perturbation and observation (P&O) for photovoltaic (PV) power generation. Using the MPPT technology, solar PV cells may produce their maximum power output. By utilizing solar radiation and PV cell temperature as input factors, the method establishes the ideal duty cycle in order to guarantee the greatest output regarding DC-DC boost converter. The Proportional Integral Derivative of Fractional Order (FOPID) optimized using SBOA or Secretary Bird Optimization Algorithm is the basis for the method of Selective Harmonic Elimination (SHE) approach utilized by RSS-MLI to eliminate harmonics from the output voltage. Inspired by secretary bird survival behaviour in the wild, SBOA is a metaheuristic algorithm that is based on populations. The SBOA is utilized for optimizing the FOPID controller for selecting the optimum duty cycle of the RSS-MLI for the total harmonic distortion (THD) reduction. Performances are assessed using the proposed method in MATLAB/Simulink implementation and contrasted with traditional methods such as GA-PID, Controllers for WSOA-PID and WSOA-FOPID.

Heart disease is one of the diseases that is responsible for the death of millions of people each year worldwide. It is considered one of the main diseases in middle aged and elderly people. The increasing rate of heart disease cases, high mortality rate, and medical treatment expenses necessitate early diagnosis of symptoms. Prediction of cardio vascular disease is a critical challenge in the area of clinical data analysis Data science related Diagnosis and prediction of heart related diseases requires more precision, perfection and correctness. Deep Learning (DL) models are becoming increasingly popular for use in a wide range of clinical diagnostic tasks. Making accurate predictions is essential for such tasks because the results can have a big impact on patients and reduce mortality. DL algorithms for efficient identification of heart disease plays an important role in healthcare, especially in cardiology. Previously Machine learning algorithms used for feature extraction has some problems, there are less efficient for complex risk stages and increased computation time, and Feature extraction is inaccurate for classification, and unreliable. When extensive data exist, deep learning techniques can overcome some of those limitations. To overcome the issues, the performance of the deep learning based Software Cost Estimation Technique using SoftMax deep Scaling Gated Adverbial Neural Network (SmDSAN2) for ac curate software cost estimation. Initially we collected the dataset form standard repository and initially we started the first step is data pre-processing for reducing null and unbalance values based on Min Max Z score normalization (Mm Z score). To utilizing the feature margin range is using the threshold values and it�s based on Fuzzified Support Margin Impact Rate (FSMIR). And third step is Feature selection based on threshold values for selecting the maximum weighted range and also selecting the nearest values based on Particle Swarm Intelligence (PSI). It iteratively assigned to ranks feature importance, removes the least important, and rebuilds the model until desired feature subset is obtained. Final stage is classification is based on SoftMax deep Scaling Gated Adversial Neural Network (SmDSAN2) is evaluating the heart disease risk prediction and reducing the false rate for analysing the It has the ability to predict the risks based on SmDSAN2algorithm has shown high accuracy for Predict the dataset . Predicting techniques used by categorizing the approximation of risk level developed using Fuzzified margin rate to identify in multiclass dataset. SmDSAN2algorithm will help the Healthcare environment to follow rules standard and also reduce the risk.

Artificial Intelligence empowers the assessment of transportation, estimation, and stock management not leading to more products that ending with reduced waste. This research deals with analysis of information so as to develop a system with reduced error, enhanced benefit to the venture. This also paves way for reducing of food waste in supply chain unit. Contrasting, online food supply affects the environment also. As an outcome, new methods in machine learning can possibly improve the productivity of food organizations, likewise diminishing their ecological effect. These make the financial gain for the organization like garbage removal, which leads to reduced carbon impression.

Early detection of mental depression prevents the severe repercussions of long-term depressive symptoms such as suicidal thoughts and ideation. With the widespread use of social media and the internet these days, prompt identification of emotional reactions is essential. Therefore, monitoring social media texts like Facebook comments or tweets could be highly helpful in detecting the mental depression. With the advent of Artificial Intelligence (AI) techniques it is possible for the early detection and classification of mental depressive detection and suicidal ideation. The proposed approach uses the labelled twitter tweets to classify the depression intensity. The performance evaluation is done based on the four ensemble models known as CNN, LSTM, LSTM+ RNN and BERT for the classification of tweets based on depressive and non-depressive classes. The parameters used for the evaluation are accuracy, precision, recall and F1-Score. From the result analysis it is inferred that average high accuracy and precision is obtained as 97% for LSTM. Similarly average high recall and F1-Score is obtained as 96% and 97% respectively. Furthermore, the optimization helps to enhance the proposed classification as well as it makes suitable for identification of suicidal ideation. The suggested method thus achieves the better performance for the earlier identification mental depression based on emotions in many social media users, demonstrating the viability of CNN, BERT, RNN and LSTM.

Lymphoma diagnosis relies on accurate and timely identification of lymphoma patterns, and Content-Based Image Retrieval (CBIR) systems leveraging medical imaging have shown promise in this regard. This study proposes a novel framework that integrates deep learning and traditional learning methodologies to enhance CBIR systems for lymphoma diagnosis. The approach uses convolutional neural networks (CNNs), a type of neural network that is deep, to derive a high degree information from photos of lymphoma. For the categorization and extraction of related lymphoma photos, these attributes are subsequently fed into conventional learning algorithms like support vector machines (SVMs) or random forests (RFs). The suggested system makes use of the advantages of both conventional learning as well as neural networks. Deep learning models capture complex and abstract features from lymphoma images, enabling improved discrimination between lymphoma subtypes. Meanwhile, traditional learning algorithms provide transparent decision rules, enhancing the interpretability and trustworthiness of the CBIR system. Through the integration of these techniques, the developed system aims to facilitate efficient analysis and retrieval of relevant lymphoma images, aiding clinicians in the analytic development. The performance of the system will be compared against baseline deep learning models and traditional learning approaches separately. Evaluation metrics such as precision, recall, and accuracy will be employed to assess the retrieval performance and diagnostic accuracy of the CBIR system. The anticipated outcome of this research is an improved CBIR system that enables clinicians to effectively identify and retrieve lymphoma images.

The requirement for effective pattern and insight extraction has grown critical due to the\nexponential growth of social media data. The authors of this study offer a fresh strategy to\novercome this difficulty by using deep learning methods on massive amounts of social media\ndata stored in different places. To get valuable insights into user behaviour, preferences, and\nsocial interactions, the primary goal is to facilitate the fast and accurate detection of relevant\npatterns, feelings, and trends. The proposed system integrates distributed computing with\ndeep learning approaches to handle and analyse massive volumes of social media data\nconcurrently . Leveraging the power of distributed systems significantly improves both\nscalability and processing speed, enabling real time or near real time analysis of dynamic\nsocial media material. Unstructured text, video, and user interactions are only some of how\nsocial media data differs from traditional datasets, and the deep learning models are altered\nand improved to manage them. This research presents an approach to evaluating a CNN\nmodel\'s interpretability. The suggested Dolphin Echolocation Algorithm (DEA) was added\nduring the feature selection phase and used to fine tune the CNN\'s filter weights. Through a\nprocess of backtracking analysis on model prediction results, the approach we present can\nconduct multi angle analysis on the discriminant outcomes of multi classified text and multi\nlabel classification tasks. Data diversity, volume, and velocity challenges are typical in large\nscale social media datasets, and this study helps address those issues. The suggested method\nalso strives to use as few resources as possible, which reduces costs and helps the\nenvironment both of which are crucial when dealing with distributed databases of any size.\nExtensive tests are performed on various datasets obtained from major social media sites to\nvalidate the efficacy and efficiency of the proposed framework. The outcomes show that our\nstrategy is more precise, faster, and scalable than the status quo.

IoT traffic data can be used to apply deep learning technology to agriculture. The application of deep learning algorithms in agriculture has improved, resource management, and decision-making with positive outcomes. Precision agriculture can assist regulate crop yields by applying nutrients only when necessary to enhance crop quality and lessen adverse environmental effects. This is made possible by IoT capabilities. Identifying redundant data traffic remains a major research challenge in the field of IoT-based agricultural automation, despite the fact that numerous solutions have been offered. Additionally, farmers do not receive the necessary information about water levels, soil conditions, etc. However, using standard methods of processing makes it challenging to handle the complexity and dynamic of low data transfer. To overcome these issues, the suggested Gated Recursive Deep Neural Network (GRDNN) makes use to increase the prediction accuracy of network traffic in an IoT environment and transfer learning to address the issue of insufficient IoT Agriculture data. Then the behavioural support factors of features are filtered through Entity Spectral spider optimization algorithm to find the relational feature weight. Then fitness function evaluates the scalar variations to get the support index based on the behavioural variation. The features get indexed by class by reference to the average mean weight and selected through Cluster-Scalar Entity Feature Selection (CSEFS) model. The selected features are progressed into Soft-max Neural Network (SMNN) which is optimized with GRDNN classifier. This optimization evaluates the entity scalar values to train the features in the Gated Recursive Deep Neural Network (GRDNN) to classify the result. The proposed system predicts the threading levels to categorize the Risk level based on features threshold margins to improve the security as well in IoT. This proposed system achieves high performance compared to the other system in precision rate with least false rate to attain the accuracy.

CVD is responsible for numerous deaths worldwide and impacts millions of individuals annually. Its impact spans all demographics, making it a major public health concern worldwide. In CVD, the heart does not pump sufficient blood to the rest of the body. A precise analysis of CVD is critical for its prevention and treatment. CVD encompasses four categories, including coronary heart disease, transient ischemic attack, peripheral artery disease, and aortic disease. These symptoms often manifest in the elderly and can be mistaken for other diseases, making an accurate diagnosis challenging and potentially leading to fatal outcomes. ML and DL algorithms play a crucial role in disease analysis, as they can categorize or predict outcomes. However, a common challenge in ML is the high dimensionality of data, which complicates feature selection. Thus, operations performed on this data require more training, leading to training loss, low precision, and recall rate to degrade the detection accuracy. An Autoencoder Methodology Combined with the Self Organizing Maps (Auto E-SOM) algorithm was proposed to resolve the issue. Initially, the CVD dataset at the Kaggle repository includes the healthcare margins. The proposed system works under pre-processing, feature selection, and classification phases. The CVD dataset will be pre-processed based on the normalization method in the starting phase. It converts features in CVD datasets to a standard scale, improving the efficiency and accuracy of output values. The primary purpose of normalization is to remove possible biases and distortions that may occur due to differences in feature sizes. The second phase is featuring selection; the relevant features related to CVD are extracted from the dataset using the SVM method. The third phase is classification. The Auto E-SOM assists in comprehending the fundamental structure of the dataset, recognizing patterns, and enhancing classification accuracy through prioritizing the most significant features. This novel aims to develop a reliable and accurate CVD detection model, which could contribute to healthcare specialists in initial diagnosis and interference, eventually leading to improved patient outcomes. Investigational outcomes determine that our method beats the previous method in precision, recall, F1 score, accuracy, and time complexity.

According to the International Diabetes Federation (IDF), diabetes is a chronic condition that is just one of the glaringly increasing global health issues of our day. Unprocessed diabetes can harm the kidneys, nerves, and heart and initiate eye illnesses like diabetic retinopathy. The healthcare cost of diabetes was 760.3 billion in 2019 and is projected to upsurge to 824.7 billion by 2030, according to IDF. Identifying people at risk can counteract health issues, improve the superiority of life, and hold back many costs. In healthcare systems, the wrong assortment of lesser groups of patients is costlier than the misclassification of healthy individuals. However, ML algorithms assume a uniform misclassification error and a symmetric class distribution. It is often difficult to analyse diabetes data to predict the onset of diabetes. A Recurrent Neural Network with Radial Basis Function Networks (RNN-RBFN) method based on DL methodology is proposed to resolve the issue. Initially, we collected the diabetic dataset at the Mendeley Data website, which includes a healthcare margin. The proposed method has three stages: pre-processing, feature selection, and classification. In the first stage, the diabetic dataset will be pre-processed based on the Imputation method. It is used to swap the absent data in the dataset with alternative characteristics so that the maximum of the data or info in the dataset is retained. This method is used because it is impractical to remove data from the dataset every time and can significantly reduce its size. The next stage is featuring selection, which uses linear discriminant analysis (LDA) to plot the data in a low-dimensional space to maximize class separation. This is accomplished by finding a set of linear discriminants that maximizes the proportion of between-class variance to within-class variance. The last stage is classification; RNN-RBFN can learn more complex patterns and improve classification performance compared to using either network in isolation. Investigational outcomes determine that our method beats the previous method in precision, recall, F1 score, accuracy, and time complexity.

A major health problem affecting the population worldwide is heart disease. As a result, it is difficult for the heart to function normally and can eventually lead to heart failure, as this condition typically impairs the heart\'s ability to circulate blood efficiently throughout the body. A significant part of the impact on quality of life is due to the complexity of early heart disease diagnosis methods. A timely diagnosis of heart disease is vital to preventing and treating heart failure effectively. A prognostic method for detecting cardiac abnormalities can be used to determine the likelihood of developing heart disease. However, one of the most critical and complex tasks is to predict heart disease risk factors with sensitivity, specificity, accuracy, and false scores. To solve this problem, we propose an Enhanced Support Vectorized Machine-based Risk Factor (ESVMRF) technique to more accurately predict heart disease risk levels. Furthermore, we use the Standardized Min-Max Scalar (SMMS) method, a data normalization technique, with pre-processing to analyse the missing attribute values in the heart disease dataset. Furthermore, outliers can be removed by analysing the upper or lower bounds using the Inter-Quartile Range-Based Outlier Removal (IQROR) method. Next, we employ the Relation Feature Weight Vector (RFWV) algorithm, a feature selection technique, to select the optimal features and analyse weights. Using Machine Learning (ML) techniques, the proposed ESVMRF method can be used to predict the risk level of heart patients based on ML techniques to differentiate between healthy patients and patients with heart disease. Further, the method has been shown to be able to predict the risk factors for heart disease patients based on metrics such as sensitivity, specificity, accuracy, precision, F-score, and performance evaluation. The accuracy of this method increased to 95.4%, which provides a valuable and reliable method for predicting heart disease.

The diagnosis of schizophrenia during early stages allows for better treatment results becauseof its complex debilitating nature. The treatment and diagnosis of schizophrenia early on canbenefit greatly from machine learning model applications. The proposed data processingapproach incorporates SMOTE (Synthetic Minority Over sampling Technique) for addressingclass imbalance together with Normalization for standardizing feature values. A hybrid modeldesign made up of Lasso Regression for feature selection and CNN Logistic Regression forclassification helps process both imbalanced classes and features with different scales in thedataset. Lasso Regression serves as an algorithm to select important features from datasetswhich enhances both the interpretability and the predictive power of the model. Theintegrated system applies CNN components for extracting complex neuroimaging patternsfrom MRI data followed by Logistic Regression models to produce binary schizophreniaversus non schizophrenia predictions . Space hierarchies of features are discovered efficientlyby CNN operations allowing Logistic Regression to perform its linear classification duties.The two powerful analysis techniques of traditional machine learning combined with deeplearning in CNN Logistic Regression create a comprehensive and efficient approach to schizophrenia diagnosis. Utilizing the proposed hybrid approach produces superiorclassification performance compared to existing methods according to initial results thusproviding a valuable tool for clinical applications in schizophrenia detection.

This study argues for a Korean origin of the Silla face-inlaid glass bead (Treasure No. 634), challenging previous claims of Indonesian or Roman origins. Analysis of its design elements – distinctly Korean motifs – and Korean archaeological evidence of glass production techniques support domestic production during the 4th-6th centuries. The hair topknot, flower decorations on both sides, and bird and flower decorations were confirmed to be unique Korean elements, and the glass government office Japgong-sa of Silla, Korea, who excelled in inlay and metalwork, installed Japgong-ho in Wae, Japan. And the tradition of using face design or beads as necklaces or worships have started in the Hongshan culture (BC 4,500). This study established the Silla face-inlaid glass bead as a product of Korea's advanced craftsmanship and distinct cultural identity, illuminating its location & significance within East Asian trade networks.

This research aimed to describe the Positive Mental Health (PMH) of Mexican mothers. A non-experimental, empirical, quantitative, and descriptive research design was used. One hundred female participants participated. Inclusion criteria included residing in a selected community and having children enrolled in primary school during the research period. The Positive Mental Health scale, valid for the Mexican population, was applied. Results were obtained using SPSS version 26, using descriptive statistics as parameters, measurements, and percentages. The factors with the highest PMH are problem-solving and self-actualization; self-control is the factor with the lowest PMH.

Several studies point to the benefits achieved by companies that seek to innovate and, more recently, achieve this by practicing Open Innovation (OI), a management model that promotes development by seeking innovations from the creation of external partnerships (people and organizations). Despite this, many difficulties are also reported by these companies, especially when they need to measure their results in the face of these practices. In Brazil, when the search for innovations is brought to the segment of Micro and Small Enterprises (MSEs) or TechnologyBased Companies (TBCs), more difficulties are encountered, however, according to recent research, these companies resist in the face of these new scenarios of change, managing to adapt, generating and multiplying jobs, income, technology and innovation. Thus, a set of studies is proposed to analyze and propose strategies for OI implementation in MSEs and/or TBCs in Vale do Paraíba, São Paulo, Brazil and surrounding cities, as well as to verify the possible benefits that this management model provides. To achieve this goal, some methodological strategies are proposed to map the literature to recognize the state of the art, theoretical propositions and empirical tests to test the model and proposed solutions. Then, as a research method, the Multiple Case Study supported by Survey adopted techniques that allow the comparison of several subjects and scenarios. With the proposal, it is intended to assist the actors in the entrepreneurship and innovation ecosystem in decision-making, as well as to develop and multiply strategies for the competitiveness of the target sector.

Kelantan and Terengganu are rich with beautiful carvings and distinctive motifs. Typically they use motifs of flora, geometry and calligraphy rather than other motifs and also use as a benchmark of local architecture identity in Peninsular Malaysia. This paper will present a study on design analysis of carvings and motifs used on Twelve Pillared House in Kelantan and Terengganu. Carving in Twelve Pillared House in Kelantan and Terengganu is measured and referred to drawings of the Malay Studies Centre (KALAM) UTM, Skudai. The analysis is focused on some component parts of the houses which has variety types of carved motifs such as the doors and windows, railings, walls, ventilation panels and stairs. Placement of carved motifs will be studied as well as its importance to the aesthetics of a house or even reflect local regional nature of both states.

Similar to other activities, an indispensable element of success in football is financial sustainability. Naturally, financial sustainability of football clubs is directly dependent on their fans and hence the fans support. Secondary data of the study reveals that fans potential and their support in Northern Cyprus closely related to its population proved not to be sufficient for the financial sustainability of football clubs. Furthermore, primary data shows that a small percentage of the population is member of football clubs. Unfortunately, respondents of research do not have positive attitude towards supporting football clubs in almost all respect. Possibly, this attitude is the result of their assertion that football clubs are not yet institutionalized in order to create a financially sustainable status.

In Mobile Ad hoc Networks (MANETs) where nodes have limited transmitting power, the transmission is typically multi-hop. The network topology changes frequently due to the unpredictable movement of mobile nodes, since each node is free to move arbitrarily with different speeds. Thus, when one node enters the transmission range of the other node a link between those two nodes is established, and an existent link is broken when either node is out of the transmission range of the other. We refer as link duration the time interval during which the link rests functioning.\nIn this paper presents a novel mobility metric for mobile ad hoc networks, called link duration (LD) that measures the stability of an active link. This mobility metric is introduced to represent relative mobility between nodes in multi-hop distance.

Tourism is an industry in which to develop, needs sufficient knowledge\nAbout economic. Social and cultural aspects of studding region. Chahbahar city is located in extremity of south-east of Iran in a virgin environment and far from populated and busy cities, considering environmental character, cultural and human attractions( with 228 tourism a attractions, includes of 74 natural attractions, 44 cultural historical attractions and 110 man-made ones), nearness to Persian gulf countries zone , India, Afkanestan, Pakistan and also to have free commercial-industrial zone, it can invert to an important tourism pole in this region by helping of scientific management and programming. Resulting analysis shows that despite of potential bearing to convert in to decent tourism pole, this region suffers for multiplicity of decision making centers and also infrastructure shortages and lack of propaganda, which are substantial obstacles for achievement to a decent tourism setting.\nHowever, employment potential, foreign currency making, fundamental investment, existence of monument, sport entertainment attractions, bearing sandy coast, and natural views, which are positive aspects alongside development and reform of management organizations possibility, propaganda efforts, interaction between free zone authorities and tourism related organizations which supports the natural setting can promote the tourism opportunities.\nAnd also cooperation between people and ecotourism development factors, are the most important strategy for developing of tourism in this region.\nIn this essay, it has tried to specify morphology setting in relation to sustainable development about tourism industry and its effects on regional tourism programming with the help of system abilities of geographical information (Arc GIS). However, Final results Shows the most impressive factor in relation to tourist attraction is civic and residential morphology. It needs more investment on tourists comfort and tranquility condition, for presenting monuments and visual area to them.

The (jurisprudential) legal formula is a general order which is used to deduct the order of minor attributes and the Principle of no harm is one of the jurisprudential rules the governing of it has been already proved and the four evidences (the (Holy) Book, tradition, reason and consensus ) evidences are its proof and the jurisprudents have tried to interpret the Principle of no harm from various angles including the concepts of (harm, damage and No) in a way that would proximate the view of the jurisprudential legislator; although there have been discussions among them on the meaning of harm, damage and No; they are unanimous in its general contents; that is, no legitimacy in damaging another (person).\nThis paper has made researches on the view of the jurisprudents, the canonical scholars and legislators and it has observed that the legislator has used the Principle of no harm in passing the laws and most jurisprudential attributes of Principle of no harm have been also used in the law as well.

This paper seeks to provide a tool for decision makers to make more informed decisions regarding their outsourcing decisions and selection of the appropriate supplier. Fuzzy theory provides a proper tool to encounter with uncertainties and complex environment. This research adopts the fuzzy AHP as the analytical tool that determines the weights of each criterion. Then this research uses the fuzzy TOPSIS method for selection of the best supplier. The selection of appropriate supplier is a multiple attribute group decision-making exercise. In a real decision process, there are many uncertainties and ambiguities, that decision makers cannot always make precise judgments. The numerical example demonstrates that the fuzzy AHP and fuzzy TOPSIS approach can be used to simulate the decision process in supplier selection. From our research results, the quality and price are the most important criteria for supplier selection. Moreover, the results demonstrate that supplier 4 is best supplier for company of case study. The procedure proposed here can help companies to identify the best supplier. Selecting appropriate suppliers for every company are Critical and important. Therefore, there is a need for a quantitative method to help decision makers to make better decision on supplier selection. That leads to the formulation of this paper. The fuzzy AHP and fuzzy TOPSIS methods can assist decision makers to make better decisions in supplier selection.

Recently, the penetration of Distributed Generation (DG) at medium and low voltages in utility networks is increasing in developed countries and takes special place for them worldwide. Due to the DGs advantages, including using of Renewable Energy which not polluting environment and has endless nature, the application of DGs can potentially reduce the need for traditional system expansion, controlling a potentially huge number of DGs creates a daunting new challenge for operating and controlling the network safely and efficiently. One problem that encountered with it when using with DGs, is an unwanted Islanding phenomenon. In this paper, a active technique for inverter based distributed generations using positive feedback of rate of change of negative voltage sequence of voltage in order to detecting islanding conditions has been present. The simulation results performed in MATLAB, clearly show improved operation of this method.

Supply chain management is the management of internal and external processes or functions to satisfy a customer’s order from raw materials through conversion and manufacture through shipment. Recent studies indicate that supply chain performance affects more than 85 percent of a manufacturer’s costs and a large percent of its revenues. Monitoring this performance through measurements is, therefore, practical and helps to identify optimization opportunities. Performance measures, or “metrics,” are used to monitor the progress of supply chain initiatives. In other words, a performance measure is a value or characteristic to measure output or outcome. In this study, using improved Willis method and base on Gunasekaran Model, has been presented practical method that calculates degree of supply chain management performance. The measurement framework in this study offers guidelines for measuring the supply chain performance in manufacturing units. The case study of this research is relevant to performance measurement of supply chain management in MAHER ANDISH unit which is one of the largest motor vehicle parts manufacturers in Iran. Based on Gunasekaran Model, supply chain management levels were divided into 3 levels and 15 criteria. Findings indicate the degree of supply chain management Performance in this industry unit is equal to 0.783.

Manufacturers need to optimize operations. One of the best solutions for optimizing is achieving the highest possible degree of adaptability to lean manufacturing characteristics. The basic lean manufacturing elements include production flow, organizing, process control, measurement and supporting. Among these elements, measurement is of special significance. Measurement in lean manufacturing refers to determining the rate of adaptability in a system with lean manufacturing characteristics and hence, determining the degree of compatibility with criteria and characteristics of lean manufacturing, so manufacturers should constantly assess the degree of adaptability of their systems to lean manufacturing criteria.\nPurpose– The purpose of this paper is determining ATO systems leanness. Most previous studies have been done in Manufacturing To Order and Manufacturing To Stock industries, While The present study has been done in Assembly To Order or ATO industries.

A process of modern designing named Direct Strength Method (DSM) for the thin-walled steel sections with slender components has been investigated. Thus, by perfectly understanding the notions and the fundamental principles, a background has been provided for developing and codifying the Iranian regulations for designing the formed sections from the sheets with slender components. Subsequently, using this approach for predicting ultimate strength of slender sections needed conducting the numerical sustainable analysis. It is also required to determine the behaviour of elastic buckling of the corresponding member in a range of actions and reactions of local, distortion and general buckling modes which in order to simulate such this behaviour, the sections are analyzed through CUFSM software based on the finite strip method(FSM). \nAs an illustration, a sample of a thin walled section with slender components (C-shaped flange) is solved so as to compare the accuracy and precision of DSM relations, governing on the components of thin-walled members through considering the effect of limited stress factor (ρ) under the uniform pressure, with the regulations of designing thin-walled members based on the effective breadth method (AISI).

In this investigation, amodern process of designing named “Direct Strength Method” (DSM) for the columns with welded steel sections formed of the sheet with slender components under uniform compression based on AISC regulations has been developed and reviewed. A set of theoretical and software studies have been conducted on the welded slender sections of box and H shaped with theconventional steel sheets with the constant thickness of 8 (mm) and yield stress of 2400 (Mpa) in order to reach the ultimate strength of the aforementioned columns.Subsequently, using DSM for final predicting such sections like these needs numerical stability analysis and it is also required to determine the behavior of elastic buckling of the member in a range of actions and reactions related to local and general buckling modes. Hence it is mandatory for the sections to be analyzed through CUFSM software based on the finite strip method (FSM) in order for simulating the mentioned behavior.So as to complete the basic information, the effect ofinitial imperfections and defects on the applied sections and the behavior ofpost-buckling strength has been surveyed through using non-linear analysis of ABAQUS software based on finite element method (FEM) with the assumption ofno effect of residual stress. Eventually, the accuracy and precision of reviewed DSM equations have been compared to the final amounts obtained from AISC regulations for columns with slender sections.

Genetic algorithm is a particular type of evolutionary algorithms in which the biologic techniques like genetic and mutation are used. As a matter of fact, Inspired by Darwinian principles of natural evolution to find optimal formula to forecast or adjust the pattern -these principles (Darwinian principles) are used. Genetic algorithm is a programming technique in which the genetic evolution is used as a pattern to solve the problem. The problem which has to be solved is the input in which based on a pattern - the solutions encoded and called the “fitness function“, each solution evaluates the candidates chosen randomly. An evolutionary algorithm is a sub-field of evolutionary computing to find the optimal solution which it has been considered for various problems. Along this, the concepts which are applicable in present paper are the concepts which are on the basis of computer science and Genetic science like the algorithm and various types of it, search, heuristic, the history of Genetic algorithm and Genetic science, Gene, Chromosome and inheritance. In present paper, it has been attempted to observe the application of Genetic algorithm in heat flow.

Empowerment makes staffs to think that organization and job belongs themselves and to be proud of working there. Empowerment of human resources is an essential issue, as a modern approach for internal motivation of job to release potential power of staffs and providing opportunities to show talents, powers and competence of individuals. On the other hand, empowerment would lead to improvement of service quality, validation of organization’s efficiency, and enhancement of staffs’ motivation. Hence, the organization would be able to cope with changes through including such committed and motivated staff. The main objective of the present study is to identify effectiveness of psychological empowerment on organizational commitment. Case study has been conducted on Steel Alborz Co Iran. Applied method in the present study has been field method, which have been used through questionnaire and survey method for data collection. Statistical population of the study has been 243 staffs of Steel Alborz Co and all population has been considered, since population size has been limited. The study includes 1 main hypothesis and 12 secondary hypotheses, which have been analyzed using two programs of SPSS and LISREL. Obtained results from the study indicate that there is a significant relationship between elements of psychological empowerment such as effectiveness, right to choose, job competence with emotional commitment.