Solving Techniques Research Articles

Simulation and validation of the transmission error, meshing stiffness, and load sharing of planetary spur gear transmissions

Although the load sharing between planets of sequentially phased planetary gear transmissions has been studied in the past, the required solving techniques based on the Finite Element Method result in long time consuming and high computational cost. This limits the possibilities of undertaking extensive studies that take into consideration a high number of cases allowing optimal solutions to be sought or general conclusions drawn. In addition, the determination of the curves of transmission error, time-varying mesh stiffness, and load sharing among tooth pairs in simultaneous contact are also complicated. In this work an analytical model has been developed for the simulation of the time-varying mesh stiffness, quasi-static transmission error, and load sharing ratio between planets and tooth pairs of planetary spur gear transmissions. It is based on similar models for external and internal spur gears previously developed and has been validated by comparison with a hybrid model based on the Finite Element Method and theoretic-experimental correlation.

Group Guidance with Problem-Solving Techniques to Prevent Suicidal Ideation

Aim: determine the differences in the level of suicidal ideation before and after the implementation of group guidance on students at Muhammadiyah Middle School, Palangkaraya, (2) determine the effectiveness of group guidance in preventing suicidal ideation in students at Muhammadiyah Middle School, Palangkaraya. Method: The population in this study were all students of class VIII of SMP Muhammadiyah Palangka Raya, Central Kalimantan, totaling 71 students. The sample of this study consisted of 9 students out of 21 students of class VIII-C. Sampling was determined by the purposive sampling technique. Result and Discussions: The results of the study showed that: (1) before being given group guidance services, most of the students in class VIII-C, namely 9 students, experienced suicidal ideation with a moderate category. After being given group guidance services with the Problem Solving technique, most of the students, namely 5 students with a low category, and 4 students with a moderate category. The total Pre-test score was 587 and the Post-test score was 437, the total score difference was 150. This means that many changes occurred after the provision of group guidance services by the researcher; (2) the results of the hypothesis test calculation using SPSS 25 software on the paired sample T-Test method showed that the significance value obtained was 0.001. This shows that the Sig value <0.05, which means that there is an influence of Problem-Solving to prevent suicidal ideation in class VIII.C students of SMP Muhammadiyah Palangka Raya.

Криминалистическая профилактика — нелюбимое дитя. Версия 2.0

The change in the crime structure in the past 10 years caused by the development of digital technologies requires new methods, means and techniques of solving and investigation of crimes. Review of crime prevention from the standpoint of regularities covered by the subject area of the criminalistic science refl ects the need to combat crime by criminalistic methods and means. The content, methods and forms of criminalistic prevention diff er from criminological ones as they are conditional upon the modus operandi.

Wild raccoons demonstrate flexibility and individuality in innovative problem-solving.

Cognitive skills, such as innovative problem-solving, are hypothesized to aid animals in urban environments. However, the significance of innovation in wild populations, and its expression across individuals and socio-ecological conditions, is poorly understood. To identify how and when innovation arises in urban-dwelling species, we used advanced technologies and new testing and analytical methods to evaluate innovative problem-solving abilities of wild raccoons (Procyon lotor). We deployed multi-compartment puzzle boxes with either one or multiple solution types and identified raccoons using radio frequency identification. Raccoons solved these novel extractive foraging tasks, and their success was influenced by age and exploratory diversity. Successful raccoons always discovered multiple different solution types, highlighting flexible problem-solving. Using a unique, comparative sequence analysis approach, we found that variation in raccoon solving techniques was greater between individuals than within individuals, and this self-similarity intensified during times of competition. Finally, the inclusion of an easier solution in the multi-solution trials enabled previously unsuccessful raccoons to bootstrap their learning and successfully open multiple difficult solutions. Our study suggests that innovative problem-solving is probably influenced by many factors and has provided novel field and analytical methods, as well as new insights on the socio-ecological dynamics of urban populations.

Pelatihan Pengelolaan Emosi pada Anggota Bintara Remaja di Polresta Surakarta

Teenage non-commissioned officers face several challenges in managing their emotions, which can hinder their ability to perform their duties optimally. One of the main challenges is lack of experience. As newcomers, young non-commissioned officers may not be used to dealing with various stressful, dangerous situations or involving interactions with the public. Emotion management training is expected to provide a number of important benefits for juvenile non-commissioned officers and the police institution as a whole. One of the main benefits is increased performance. To achieve the expected goals and benefits, the implementation of emotion management training must be carried out systematically and continuously. Training can begin with an introductory session on the importance of managing emotions, followed by practical training on communication skills, problem solving, and relaxation techniques. In addition, training should include simulations of real situations that youth NCOs may encounter, so that they can apply the skills learned in relevant contexts. Training evaluations must be carried out periodically to ensure that training objectives are achieved and the expected benefits can be felt. Evaluation can be done through knowledge tests, performance observations, and participant satisfaction surveys. Overall, this emotion management training provides short-term benefits in the form of reduced stress and improved performance, while also providing youth non-commissioned officers with skills that are crucial for their long-term careers in the police force. Thus, investment in the development of these skills not only improves operational efficiency but also supports the psychological well-being and professionalism of future police officers.

Efficient enhancement of low-rank tensor completion via thin QR decomposition.

Low-rank tensor completion (LRTC), which aims to complete missing entries from tensors with partially observed terms by utilizing the low-rank structure of tensors, has been widely used in various real-world issues. The core tensor nuclear norm minimization (CTNM) method based on Tucker decomposition is one of common LRTC methods. However, the CTNM methods based on Tucker decomposition often have a large computing cost due to the fact that the general factor matrix solving technique involves multiple singular value decompositions (SVDs) in each loop. To address this problem, this article enhances the method and proposes an effective CTNM method based on thin QR decomposition (CTNM-QR) with lower computing complexity. The proposed method extends the CTNM by introducing tensor versions of the auxiliary variables instead of matrices, while using the thin QR decomposition to solve the factor matrix rather than the SVD, which can save the computational complexity and improve the tensor completion accuracy. In addition, the CTNM-QR method's convergence and complexity are analyzed further. Numerous experiments in synthetic data, real color images, and brain MRI data at different missing rates demonstrate that the proposed method not only outperforms in terms of completion accuracy and visualization, but also conducts more efficiently than most state-of-the-art LRTC methods.

Effect of Using Problem Solving Technique of 5Es Instructional Model on Student’ Learning at Secondary Level: An Analysis

The study aimed to analyze,” Effect of Using Problem Solving Technique 5Es Instructional Model on Student’ Learning at Secondary Level”. The descriptive part was based on data collected through the questionnaire and the interview protocol from head teachers, teachers and students of class 10th. The experimental part was based on data collected through pre-test and post-test for 9th class students. The (QUAN-QUAL) method was executed and the explanatory sequential method was used. Four 4 Head teachers,16 English Teachers and 400 students200 girls and 200 boys studying in government schools in tehsil and district R.Y. Khan. The stratified sampling technique was implemented. The desired data was collected from Tehsil Rahim Yar Khan. The sample, sampling and sample size of the study was comprised; 4 secondary school head teachers with the same ratio of male and female; 16 secondary school English teachers with the same ratio of male and female; 400 students of secondary classes with the same ratio of male and female. The study found that majority (55%) of respondents agreed that they were interested in using problem solving technique in teaching of English. Mean value 2.94 shows a central tendency towards neutrality with a slight inclination towards agreement, indicating a moderate recognition of the role of interest in learning among respondents. The standard deviation 1.034 indicates moderate variability in responses suggesting that while there was some consensus; opinions on the influence of interest on learning still vary among the respondents. On the basis of these findings researcher presented some recommendations.

Influence of E-Lkpd Problem Based Learning on Problem Solving Capability Students on Polluting Materials Environment

Background: LKPD used is still in printed form and is not yet varied so that students' problem solving abilities are not optimal. The purpose of this study was to determine the effect of PBL-based E-LKPD on students' problem-solving abilities at MAN 1 Indramayu. Method: This research is a quantitative research with the research design True Experimental Post-test only control design. Population in this study were all students of class X IPA MAN 1 Indramayu with a sample of class X IPA 3 as the control class and class X IPA 2 as the experimental class. Sampling technique in this study was taken using the Cluster Sampling technique. Data were collected by problem solving techniques and observation tests. Test instrument in this study was a description to measure students' problem-solving abilities. The observation sheet instrument contains problem solving indicators, PBL learning syntax, student activities, constraints, and descriptions.. Results: Data analysis techniques using the t-test (Independent Sample T-test). Independent Sample T-test showed sig. (2-tailed) < 0.05, thus proving that there is a significant difference in the average increase in students' problem solving abilities between the experimental class and the control class. Conclusion: Based on the results of the study, it can be concluded that there is an effect of using PBL-based E-LKPD on students' problem-solving abilities. Keywords : Media E-LKPD; Model Problem Based Learning; Problem solving

Keefektifan Layanan Bimbingan Kelompok Teknik Problem Solving untuk Meningkatkan Kemandirian Belajar dalam Menentukan Karir

Group guidance services are one way of providing guidance to students who have certain problems in a group. Group tutoring services can be said to be effective if students can understand and follow the guidance optimally so that they can increase learning independence. Problem solving techniques are techniques for handling difficulties faced by each individual to reduce a person's sense of lack of understanding in order to achieve success. This research aims to determine the effectiveness of problem solving technique group guidance services to increase learning independence in determining a career. The type of research used in this research is experimental research with a true experimental research design. The objects in this research are the experimental class and the control class. The sample used in this research was taken randomly as a control group with a total of 7 students and 7 students as a class. Data collection techniques used in this research include interviews and observations. The research data was analyzed using the Independent Sample T-Test and the N-Gain test on the SPSS 23 application. The research showed that the average Pretest was 129.74 and Posttest was 138.5. Based on the results of the research that has been carried out, it can be concluded that the increase in respondents' scores from pretest to posttest, giving treatment can improve the results of respondents' answers, there is an increase in learning independence in determining a career after being given treatment.

Exploring Trainees’ Behaviour in Hands-on Cybersecurity Exercises Through Data Mining

Despite the rising number of cybersecurity professionals, the demand for more experts in this field is still substantial. Cybersecurity professionals must also possess up-to-date knowledge and skills to counter cybersecurity threats’ dynamicity and rapidly evolving nature. Hands-on cybersecurity training is mandatory to practice various tools and improve one’s technical cybersecurity skills. Generally, an interactive learning environment is set, where trainees perform sophisticated tasks by accessing complete operating systems, applications, and networks. One of the main challenges that cybersecurity organizations are facing today is the generation of massive data through practice exercises. So, it becomes a problem to convert this data into knowledge to improve the overall quality of the learning system. The large amount of interaction data and its complexity also limit us to do automated analysis. Thus, these challenges for cybersecurity learners can be addressed through appropriate educational data analysis by having insights or testing hypotheses or models on a proper dataset. Revealing the patterns, rules, item sets and time taken by trainees while using any command line tool could help the trainer to assess the trainees and to provide feedback. Therefore, in this paper we are analyzing the frequency patterns and timing information captured from the trainees’ command line log to reveal their solving techniques, easy and struggling stages, slipups, and individual performance. Through our study, we aim to show how education and training providers can foresee learners who are less likely to succeed in a task or exhibit low performance, which can impede learning proficiency. With this knowledge, organizations and trainers can identify trainees who require additional attention or support. It may also be able to identify elements related to an organization like training aids, training methodology, etc. that need improvement. This study demonstrates the utility of data-mining techniques, specifically rule mining and sequential mining, to empower training designers to delve into datasets derived from cyber security training exercises.

Solving Optimal Electric Vehicle Charger Deployment Problem

Electric vehicles (EVs) have already been acknowledged to be the most viable solution to the climate change that the entire globe has long been combating. Along the same line, it is a salient subject to expand the availability of EV charging infrastructure, which quintessentially necessitates the optimization of the charger’s locations. This paper proposes to formulate the optimal EV charger location problem into a facility location problem (FLP). As an effort to find an efficient method to solve the well-known nonpolynomial deterministic (NP) hard problem, we present a comparative quantification among several representative solving techniques. This paper features two comprehensive case studies representing regions with an average and a high density of EVs. As such, this paper shows that the proposed framework can lead to successful location optimization with adequate refinement of solving techniques.

Supporting Implementation Through Online Learning Communities: Lessons Learned From a National Training and Technical Assistance Network.

Recent implementation science frameworks highlight the role of training and technical assistance (TTA) in building workforce capacity to implement evidence-based practices (EBPs). However, evaluation of TTA is limited. We describe three case examples that highlight TTA by three regional centers in the national Mental Health Technology Transfer Center (MHTTC) network. Each MHTTC formed Learning Communities (LCs) to facilitate connections among behavioral health professionals with the goals of sharing implementation strategies, discussing best-practices, and developing problem solving techniques. Data on outcomes were collected through a combination of self-report surveys and qualitative interviews. LC participants reported strong connectedness, gains in knowledge and skills, improvements in implementation capacity, and intentions to advocate for organizational and systems-level change. Furthermore, across the case examples, we identified LC characteristics that are associated with participant perceptions of outcomes, including tailoring LC content to workforce needs, providing culturally relevant information, engaging leaders, forming connections among participants and trainers, and challenging participants' current workplace practices. These findings are interpreted through the lens of the Interactive Systems Framework, which focuses on how TTA, such as LCs, can facilitate connections between the theoretical and empirical foundations of interventions and the practices of implementing interventions in real-world settings to advance workforce capacity.

Spectral neural approximations for models of transcriptional dynamics

The advent of high-throughput transcriptomics provides an opportunity to advance mechanistic understanding of transcriptional processes and their connections to cellular function at an unprecedented, genome-wide scale. These transcriptional systems, which involve discrete stochastic events, are naturally modeled using chemical master equations (CMEs), which can be solved for probability distributions to fit biophysical rates that govern system dynamics. While CME models have been used as standards in fluorescence transcriptomics for decades to analyze single-species RNA distributions, there are often no closed-form solutions to CMEs that model multiple species, such as nascent and mature RNA transcript counts. This has prevented the application of standard likelihood-based statistical methods for analyzing high-throughput, multi-species transcriptomic datasets using biophysical models. Inspired by recent work in machine learning to learn solutions to complex dynamical systems, we leverage neural networks and statistical understanding of system distributions to produce accurate approximations to a steady-state bivariate distribution for a model of the RNA life cycle that includes nascent and mature molecules. The steady-state distribution to this simple model has no closed-form solution and requires intensive numerical solving techniques: our approach reduces likelihood evaluation time by several orders of magnitude. We demonstrate two approaches, whereby solutions are approximated by 1) learning the weights of kernel distributions with constrained parameters or 2) learning both weights and scaling factors for parameters of kernel distributions. We show that our strategies, denoted by kernel weight regression and parameter-scaled kernel weight regression, respectively, enable broad exploration of parameter space and can be used in existing likelihood frameworks to infer transcriptional burst sizes, RNA splicing rates, and mRNA degradation rates from experimental transcriptomic data.

The spectra masses for heavy pentaquark using generalized fractional of the extended Nikiforov-Uvaro method

The Nikiforov-Uvarov method is an efficient technique for solving of heavy diquark systems. It has been used to derive analytic-exact energy eigenvalues and eigenfunctions in fractional forms, which are useful in describing such systems. The potentials employed include the Cornell potential, harmonic potential, and spin-spin interaction; have been updated with respect to previous studies. Mass spectra of heavy pentaquarks were also calculated using this method. Compared to previous studies, the present results exhibit good experimental data agreement and are improved. We deduce that the fractional models contribute greatly to the heavy pentaquark masses.

Solving linear systems of fractional integro-differential equations by Haar and Legendre wavelets techniques

In this study, we present two highly effective approaches aimed at solving linear systems of equations, specifically focusing on the Fredholm and Volterra equations in fractional integro-differential formula. To tackle these challenging problems, we employ two distinct methodologies: the Haar wavelet technique (HWT) and the Legendre wavelet Technique (LWT). To accurately describe fractional derivatives with the equations, we adopt the Caputo sense, which provides a robust framework for handling fractional calculus. By applying HWT and LWT, we are able to transform the intricate equations of the fractional integro-differential system into algebraic equations. Fredholm and Volterra fractional integro-differential equations serve as benchmarks for evaluating the accuracy and efficiency of LWT and HWT. By comparing the approximate solutions obtained through these wavelet methods with the exact solutions, we are able to provide a comprehensive assessment of their performance. Through detailed numerical computations conducted using Mathematical 8,which demonstrate the effectiveness of both LWT and HWT in solving systems of Fredholm and Volterra equations in fractional integro-differential equations. The results obtained from these computations are presented through graphs, allowing for a clear and comparison between the approximate and exact solutions. The successful utilization of these wavelet methods provides researchers and practitioners with valuable tools for tackling complex mathematical problems in various fields of study. This study contributes to the progress of numerical techniques for solving of fractional integro-differential equations which aid in the development of more accurate and efficient computational approaches for analyzing and solving similar mathematical problems in the future.

Astrophysics-based transit search optimization heuristics for parameter estimation of multi-frequency sinusoidal signals

This study investigates an astrophysics-based Transit search optimization algorithm (TSOA) for solving a challenging engineering problem. This innovative strategy uses the fundamentals of physics to improve the accuracy and efficacy of problem solving techniques. Harmonic distortions in power systems have become a massive challenge because of the nonlinear loads associated with the electrical power distribution system. Overheating of the equipment, motor failure, capacitor failure, and improper power metering are all issues caused by harmonic distortion. A new examination of the causes and consequences of these issues, as well as the status of hardware and software available for harmonic evaluation, is necessary in light of the unprecedented advancements in power electronic devices and their integration at all levels in the power and energy system. In order to estimate phase and amplitude simultaneously with a specified frequency, an objective function of power system harmonics is created. Keeping in mind the adverse effects of harmonics, parameter estimation is carried out under various conditions by taking different particle sizes and signal-to-noise ratios. TSOA proved its efficacy for both phase and amplitude parameters under different situations and precisely estimated the harmonics signal up to an accuracy of 1.1648E–15. Two harmonic signals were taken in this research work, and the best MSE values achieved are 9.748E–4, 8.287E–07, 6.157E–10, and 1.165E–15 for 30, 60, 90, and 150 dB noise, respectively, under case study 1 while varying the particle size from 50 to 450. The results for case study 2 proved to be best up to 7.373E–16, and no significant change occurred by increasing the generations above 500. The proposed study would be a step further in developing a more accurate and robust computing platform for robust estimation of harmonics arising in power and energy systems.

Genetic Algorithm-Based Optimisation of a Double-Wall Effusion Cooling System for a High-Pressure Turbine Nozzle Guide Vane

Double-Wall Effusion Cooling schemes present an opportunity for aeroengine designers to achieve high overall cooling effectiveness and convective cooling efficiency in High-Pressure Turbine blades with reduced coolant usage compared to conventional cooling technologies. This is accomplished by combining impingement, pin-fin and effusion cooling. Optimising these cooling schemes is crucial to ensuring that cooling is achieved sufficiently at high-heat-flux regions and not overused at low-heat-flux ones. Due to the high number of design variables employed in these systems, optimisation through the use of Computational Fluid Dynamics (CFD) simulations can be a computationally costly and time-consuming process. This study makes use of a Low-Order Flow Network Model (LOM), developed, validated and presented previously, which quickly assesses the pressure, temperature, mass flow and heat flow distributions through a Double-Wall Effusion Cooling scheme. Results generated by the LOM are used to rapidly produce an ideal cooling system design through the use of an Evolutionary Genetic Algorithm (GA) optimisation process. The objective is to minimise the coolant mass flow whilst maintaining acceptable metal cooling effectiveness around the external surface of the blade and ensuring that the Backflow Margin for all film holes is above a selected threshold. For comparison, a Genetic Aggregation model-based optimisation using CFD simulations in ANSYS Workbench is also conducted. Results for both the reduction of coolant mass flow and the total optimisation runtime are analysed alongside those from the LOM, demonstrating the benefit of rapid low-order solving techniques.

A macroscopic traffic flow modelling and collision avoidance using B-spline and Physics-Informed Neural Network approaches

The macroscopic model, such as the Lighthill-Whitham-Richards (LWR), has been extensively studied and applied to various homogeneous traffic problems. However, numerical methods have been widely used with good performance. The outcome of this work is the proposal of a one-dimensional speed-density model. We applied both the B-spline collocation and the Physics-Informed Neural Network (PINN) methods to solve this model. The results clearly demonstrated that the B-spline method outperforms the PINN method in terms of accuracy. These results were then used firstly to compare it with thus obtained with a microscopic urban mobility simulator (SUMO) and secondly to visualize collision phenomena which are crucial for public safety. To manage collisions, the Intelligent Driver Model (IDM) was implemented. This integrated approach highlights the effectiveness of our density-speed model, coupled with advanced solving and control techniques, in enhancing the understanding and management of traffic.

Self-Management Therapy and Creative Problem-Solving Technique on Smartphone Addiction among Public Colleges of Education Students in Oyo State, Nigeria

As smartphone usage rises worldwide, concerns about the detrimental impacts of smartphone addiction, particularly among college students, have grown. However, the extent to which smartphone addiction has affected College of Education students in recent years is disturbing. The magnitude of smartphone addiction does not promise well for the future, individual mental health, or the overall well-being of college students. Based on this premise, this study investigated how self-management therapy and creative problem-solving techniques affected smartphone addiction among Oyo State College of Education students. A pretest-posttest, control group quasi-experimental design with a 3x2x3 factorial matrix was used in the study. The samples included 90 students from three public colleges of education in Oyo State, Nigeria. Participants were assigned to three groups: Self-Management Therapy (SMT), Creative Problem Solving Technique (CPST), and control). The administration of treatments lasted for eight weeks for the experimental groups. Seven hypotheses were tested at 0.0experimental groups received treatments for eight weeks. At the 0.05 level of significance. Data were analysed using Analysis of Covariance. There was a significant main effect of treatments on smartphone addiction among college of education students in Oyo State (F (2, 70) = 29.47, p < .05, 𝜂2= .459). Participants exposed to SMT (𝑥̅= 28.81) had the lowest smartphone addiction mean score, while the CPST group (𝑥̅= 49.89) and control group (𝑥̅= 53.78). Self-management therapy and creative problem-solving techniques were effective in reducing smartphone addiction, but self-management therapy was more effective. It is so proposed that the two therapies be integrated into the public college of education counselling sessions and indoctrination to lower the likelihood of smartphone addiction.

Implementasi Metode EDAS Dalam Penilaian Kinerja Dosen Pada Masa Pandemi Covid-19 dengan Pembobotan Entropy

Along with the development of technology that makes online learning run well, but the enthusiasm for student learning is not like when face to face and there are still many students who do not take various courses so that students' knowledge abilities are greatly decreased. When online learning takes place, it is very necessary for a lecturer's performance to arouse students' enthusiasm in participating in online learning. Because during this pandemic the role of lecturers is very necessary to develop an innovation and be able to make students not bored in participating in online learning. And also in terms of the performance appraisal process, several problems often occur, namely the speed in the performance appraisal process and also the accuracy of the performance appraisal. In assessing the performance of lecturers during online learning, there are several criteria including Discipline, Delivery of Materials, Interaction, Discussion Questions and Answers and Punctuality. Based on these problems, a decision support system is needed as a problem solving technique and is assisted by a method that can produce an accurate final value. The method is the Evaluation Based On Distance From Average Solution (EDAS) and Entropy method which is very helpful in generating weight values ??from alternative data and criteria so as to get the final results obtained in Alternative L9 with a value of 0.31025 on behalf of Surya Darma Nst, M. Kom.