Relaxed Skolam Mean Labeling of 5 – Star Graphs with Partition 3, 2

In this article, our main topic is about the existence of relaxed skolem mean labeling for a 5 – star graph G = K1,α ∪ K1,α ∪ K1,α ∪ K1, β ∪ K1, β 1 2 3 1 2 with partition 3, 2 with a certain condition. By using the trial and error method we find the existence of the relaxed skolam mean labeling of 5 - star graph with partition 3, 2 with a specific condition.

Relaxed Skolam Mean Labeling of 6 – Star Graphs with Partition 3, 3

Existence Relaxed skolam mean labeling for a 6 – star graph 3 G = K1,α K1,α K1,α K1,β K1,β K1,β 1 2 3 1 2 ∪ ∪ ∪ ∪ ∪ with partition 3,3 with a certain condition is the core topic of the following article. Trial and error method is used to find the existence of the relaxed skolam mean labeling of 6 - star graph with partition 3, 3 holding a specific condition.

A compliance modeling method of flexible rotary joint for collaborative robot using passive network synthesis theory

Collaborative robots have become a research focus because of their wide applications. However, the previous compliance design method of the flexible rotary joint for collaborative robot mainly relied on experience of designers, and “trial and error” method is usually adopted, no feasible and systematic theory for the designer to select numerical value and series-parallel connection mode of the springs and dampers for the flexible rotary joint. Thus, developing a feasible compliance modeling theory to guide the design of the flexible rotary joint is a particularly challenging task. The main contribution of this paper is to present a novel and effective compliance modeling theory of the flexible rotary joint for collaborative robot based on electrical and mechanical passive network synthesis, to provide theoretical and systematic guidances for compliance design of the flexible rotary joint. First, inerter element is introduced into the mechanical system, and the compliance of the flexible rotary joint is expressed as an angular velocity admittance function using electrical and mechanical network analogy. Then, by passive network synthesis theory, the three kinds of compliance realization forms of rational function and four-element compliance realization conditions of biquadratic function for the flexible rotary joint are given using inerters, springs, and dampers. Moreover, numerical examples and simulations are conducted to illustrate effectiveness of the proposed compliance realization method. Finally, discussions are given to illustrate advantages of the proposed compliance modeling and design methods compared with the previous method.

Directivity Enhancement in Non-Reciprocal Bandpass Filters using an Evolutionary Algorithm

In this letter, a non-reciprocal filter with enhanced directivity is analyzed methodically and the filter parameters are optimized using an evolutionary algorithm. The return loss, insertion loss, and isolation characteristics of the filter exhibit a trade-off that makes manual tuning a trial-and-error method. The veracity of the numerical modeling is conformed by designing a 150 MHz lumped element non-reciprocal bandpass filter based on the parameters extracted using an evolutionary algorithm based particle swarm optimization (PSO). The simulated and measured results comply well with the modeling and the results exhibit maximum directivity of 28.2 dB without degradation in insertion loss (1.1 dB) and return loss (16.2 dB) within the passband. The algorithm can be utilized in designing non-reciprocal filters having different center frequencies and bandwidths.

Directivity Enhancement in Non-Reciprocal Bandpass Filters using an Evolutionary Algorithm

In this letter, a non-reciprocal filter with enhanced directivity is analyzed methodically and the filter parameters are optimized using an evolutionary algorithm. The return loss, insertion loss, and isolation characteristics of the filter exhibit a trade-off that makes manual tuning a trial-and-error method. The veracity of the numerical modeling is conformed by designing a 150 MHz lumped element non-reciprocal bandpass filter based on the parameters extracted using an evolutionary algorithm based particle swarm optimization (PSO). The simulated and measured results comply well with the modeling and the results exhibit maximum directivity of 28.2 dB without degradation in insertion loss (1.1 dB) and return loss (16.2 dB) within the passband. The algorithm can be utilized in designing non-reciprocal filters having different center frequencies and bandwidths.

Crop yield forecasting of paddy and sugarcane through modified Hendrick and Scholl technique for south Gujarat

Regression models by modified Hendrick and Scholl technique were developed on paddy and sugarcane for six districts of south Gujarat. The data on the yield and weather parameters were analyzed for 27 years. The 25 year data was used for development of the model. The validation of model was done using data set of 2010 and 2011. The stepwise regression analysis was executed by trial and error method to obtain the finest combination of predictors, significant at 5% level. The multiple regression techniques was used for fitting of the model and decided best by highest R2 and lowest percent error. All crop yield forecasting models gave good estimates and produced error percent within acceptable range. Analysis revealed that the model error percent of paddy and sugarcane for respective crop growing districts were -10.0 to 8.1% and -12.2 to 1.5% respectively. Crop yield forecasting for year 2012 based on validated model was made for the districts of Navsari, Surat, Bharuch, Valsad, Narmada and Tapi.

A mathematical model of a brushed DC motor system

Mathematical model has been proposed for some system that involves a brushed DC motor and it is widely used in industry. Brushed DC motor ideals for applications with a low- torque, manage to change pace or speed and it is widely used in many applications such as x-y table positioning system, conveyor systems and other system that required to use the features that brushed DC motor have. Mathematical model of brushed DC motor in order to verify the performance of the DC motor. In this paper, mathematical model of brushed DC motor will be derived from a brushed DC motor circuit that consist of two parts that are electrical and mechanical part. To validate the functionality of mathematical model, the performance of the brushed DC motor without any controller will be compared with the brushed DC motor with the presence of PI-PD controller that will be tuned by trial-and-error method. Performances of both brushed DC motor with and without controller will be compared in terms of transient response which are, rise time, Tr, settling time, Ts, steady state error, ess and lastly percentage overshoot. At the end of the study, the brushed DC motor with PI-PD controller show a better performance compared to the brushed DC motor without any controller.

Purpose Driven Internet Use and Its Impact on Academic Performance of College Students

The purpose of this study was to determine how students in Middle-Level Colleges use the internet, with a special focus on the Nairobi Institute of Business Studies (NIBS). The study was conducted using a descriptive research approach. The student population was determined using a purposive sample strategy, while the stratified sampling approach was utilized to divide the population into distinct subgroups (strata) based on the eight courses that students took. Both open-ended and closed-ended questionnaires were used to collect relevant data. The questionnaire was tested for validity and reliability in a group of randomly selected students. Following data collection, descriptive statistics were used to examine pertinent data. To summarize the data and report the research findings, descriptive statistics such as percentages, frequencies, bar graphs, and pie charts were used. This research focused on the NIBS College, which has approximately 3000 regular students. A total of 138 people were sampled from the population to participate in the study. The majority of students, according to the report, use the internet for entertainment. For communication and pleasure, students utilize social networking sites like Facebook and Twitter at nearly the same rates. The respondents also stated that using the internet has expanded their access to more up-to-date educational information and has had a good impact on their academic efficiency. When compared to traditional materials, respondents found the Internet to be more informative. The respondents also said that the internet saved time and provided reliable information. To access the internet, most students possess and use smartphones (iPhones). They use the internet in their dorms compared to other locations on campus or at home, where internet access was unreliable. The majority of students preferred Google to other Internet services. This demonstrated that students rely extensively on Google to find current and relevant information. The vast majority believed that people do not need to be trained on how to use the internet to find information. The trial-and-error method was the most prevalent strategy for obtaining relevant Internet skills. Others sought advice from their peers or took official training given by several computer colleges to learn how to utilize the internet.

Assessing the importance of feature selection in Landslide Susceptibility for Belluno province (Veneto Region, NE Italy)

In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (Frequency Ratio and Evidence Belief Function) and two machine learning (ML) models (Random Forest and XG-Boost) for LSM in the Belluno province (Veneto Region, NE Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least "important" features by using a common threshold. Conclusively, we found that removing the least "important" features does not impact the overall accuracy of the LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least "important" ones. This confirms that the requirement for the important factor maps can be assessed based on the physiography of the region. Based on the analysis of the three models, it was observed that most commonly available feature data can be useful for carrying out LSM at regional scale, eliminating the least available ones in most of the use cases due to data scarcity. Identifying LSMs at regional scale has implications for understanding landslide phenomena in the region and post-event relief measures, planning disaster risk reduction, mitigation, and evaluating potentially affected areas.

Facial Emotions over Static Facial Images Using Deep Learning Techniques with Hysterical Interpretation

Recognition of facial expression has many potential applications that have attracted the researcher’s attention during the last decade. Taking out of features, is an important step in the analysis of expression that contributes to a quick and accurate recognition of expression, i.e., happiness, surprise and disgust, sadness, anger, and fear are expressions of the faces. Facial expressions are most frequently used to interpret human emotions. Two categories contain a range of different emotions: positive emotions and non-positive emotions. Face Detection, Extraction, Classification, and Recognition are major steps used in the proposed system. The proposed segmentation techniques are applied and compared to determine which method is appropriate for splitting the mouth region, and then the mouth region can be extracted using techniques for stretching contrasts and segmenting the image. After the extraction of the mouth area, the facial emotions are graded in the face picture region of the extracted mouth based on white pixel values. The Supervisory Learning Approach is widely used for face identification algorithms and it takes more computation time and effort. It may also give incorrect class labels in the classification process. For this reason, supervised learning and reinforcement learning is being used. In general, it will be like a trial-and-error method that is, in the training process it tries to learn and produce expected results. It was specified accordingly. Reinforcement learning always tries to enhance the results.