Formulation Of Problem Research Articles

Transformasi Tatanan Hukum dan Demokrasi: Analisis Perubahan Struktural Pasca Reformasi

This research aims to examine the transformation of the level of law and democracy after the Indonesian reformation, of course after independence Indonesia experienced many structural changes from the pre-Reformation era to the post-reformation era. At all times, Indonesia has always tried to develop and improve Indonesia’s structural order, so this research uses two problem formulations, namely how the legal order in post-reformation Indonesia is and how democracy is implemented in post-reformation Indonesia. This research is qualitative research with a normative juridical approach. The results of this research show that the legal order in Indonesia after the 1998 reform, Indonesia experienced significant changes in its legal order and democratic system. The following are the main points related to several things, namely the constitution, government system, elections and political parties, legal developments, human rights protection, and regional autonomy. Overall, post-reform, Indonesia has moved towards a more mature democratic system with improvements in the legal order, more open elections, and increased protection of human rights.

Speech acts found in the text of Cupak and Grantang

This study aimed to investigate types of speech acts found in the text of Cupak Grantang. The text of Cupak Grantang is the text that has moral value. The theory of pragmatics in speech acts was used in analyzing the data. The data were collected based on the types of speech acts found in the text of Cupak Grantang. The data were collected based on the problem formulation in the research. Data collection methods and techniques related to types of speech acts and analysis of social context are explained as follows. In the data collection process, the author read, recorded and wrote data related to speech acts and sociopragmatic studies. After obtaining data about the types of speech acts contained in the Cupak and Garantang texts, the types of speech acts were then grouped. The results indicated that the types of illocutionary acts found in the texts are directive, commissive, and assertive. I Cupak was described as a person who always complained about what happened to him.

Stages and main tasks of the century-long control theory and system identification development. Part VI. Model predictive control

The article is devoted to one of the relatively new areas in the field of control theory and practice, called model predictive control. The ideas underlying it, the connection with the theory of optimal control and numerical methods for solving optimization problems are described. It should be noted that currently the number of problems solved and control systems developed within the framework of this approach has come out on top in comparison with all others. This is evidenced by numerous publications concerning the theory and practical application of control methods and algorithms using a predictive model. The main distinctive feature of this method is that the control is not synthesized in advance, but is calculated directly during the operation of the system, i.e. at the current time. This made it possible, with such control, to implement original feedback that ensures stabilization of the process when the model is inaccurate and there is uncertainty associated with existing disturbances and measurements. It describes how such feedback is organized based on current measurement data. In this case, there is no need to interpret somehow the nature of the disturbances and errors acting on the system. Much attention in the article is paid to the possibilities that are admitted with this method of control. The model predictive control method seems especially attractive for systems with discrete impulse processes that are well described with using cognitive maps. It is considered an example of cognitive modeling of evolutionary demographic processes in Ukraine and the possibility of control them using a predictive model, when the model contains unstable eigenvalues. When solving problems of predictive control, it is proposed to use trajectory models, which directly follow from the Cauchy formula, in particular for discrete systems, instead of a local description in the state space. This makes it possible to take into account, when solving predictive control problems, how well or poorly controlled, observable or identifiable the system is. Non-standard capabilities of this method with a trajectory description appear during terminal control on a sliding interval with a finite horizon. A number of original formulations of terminal control problems are presented in the article and some of their properties are described.

Penerapan Thinking Aloud Pair Problem Solving (Tapps) Setting Working Backward Pada Materi Belah Ketupat Untuk Meningkatkan Hasil Belajar Siswa

One common goal of learning mathematics junior high school students were included in the National Education Standards is that students have the competence to solve problems that include the ability to understand the problem , devised a mathematical model , solve the model , and interpret the obtained solution . This research is motivated by the low student math learning outcomes . This is because of the lack of creative educators in teaching mathematics so that math becomes fun. Based on those explanations, problem formulation in this research is 1)Is the application of Thinking Aloud Pair Problem Solving (TAPPS) setting working backward method into Rhombus topic able to improve learning activity of Class VII B students of MTs Miftahul Huda?; and 2) Is the application of Thinking Aloud Pair Problem Solving (TAPPS) setting working backward method into Rhombus topic able to improve learning result of Class VII B students of MTs Miftahul Huda. Meanwhile, the purpose of this research is 1) to find activity improvements of the students in the application of Thinking Aloud Pair Problem solving (TAPPS) setting working backward learning method into Rhombus topic in Class VII B MTs Miftahul Huda; 2) to find study result improvements after using Thinking Aloud Pair Problem solving (TAPPS) setting working backward method into Rhombus topic in Class VII B MTs Miftahul Huda. The research method is descriptive quantitative research method and classroom action research by data analysis technique. Based on this study result, students activity porcentage in first meeting was 87,5 % with ‘very active category’, while in second meeting the porcentage was 92,4 % with ‘very active category’. This result shows the improvement by 4,9%. Meanwhile, result analysis of students learning progress data based on data in Mathematics teacher, shows that comprehension porcentage of previous material reviews was 70,8 %, while comprehension porcentage of the last test is 87,5 %. It shows that there is improvement by 16,7 % in students comprehension at Mathematics and classically comprehensed. Based on research results and analysis, it can be concluded that 1) Aplication of TAPPS setting working backward is able to improve students activities in Class VII B MTs Miftahul Huda; and 2) Application of TAPPS setting working backward is able to improve study results of students in Class VII B MTs Miftahul Huda.

Fuzzy clustering with capacity constraints: Algorithm, convergence analysis and numerical experiments

In this paper we study the fuzzy clustering problem with capacity constraints. Despite of the fact that the fuzzy clustering approach is widely encountered in the literature, the inclusion of capacity constraints is recent and has several practical applications. We propose a general formulation of the clustering problem, where each point has an associated weight and the sum of the weights of the points that compose each group is established a priori. We discuss existence of solutions of the involved problems, providing a mathematical foundation for the established formulas. Besides, we propose a practical algorithm for solving this problem and present its convergence analysis. This algorithm follows an alternate minimization scheme, wherein a given iteration addresses the problem first in terms of the probabilities of each point xj belonging to each cluster i, denoted as uij, finding subsequently the position of the centroids, ci,i=1,…,g,j=1,…,n. This procedure is K-means-like, with the distinction that, as a point does not exclusively belong to a group, the computation of uij requires optimization techniques. In our case, this involves solving a linear system derived from the Karush–Kuhn–Tucker (KKT) conditions. With the aim of validating our algorithm, we present numerical tests with synthetic and real-world data to demonstrate its performance for the given problems. Since the proposal successfully solved these numerical tests within a reasonable computational time, it can be considered a valuable resource for addressing real-world applications.

Use of Silver Dressing on Diabetic Ulcers During Infection: Case Report

Aims: This study aimed to describe the results of applying silver dressing on diabetic ulcers during infection in patients with infected wounds on digiti one pedis dextra. Methods: This research uses a descriptive design, namely a case study with a nursing approach consisting of assessment, analysis of problem formulation, formulation of nursing diagnosis, intervention, implementation, evaluation, and nursing documentation. The subject of this case study is Mrs. A with diabetic ulcers on Digi 1 pedis extra with infection. Case management was carried out for three days on April 17-19, 2024. Data was collected through anamnesis, wound assessment, supporting examination, and literature study. Wound assessment was performed using the Meggitt-Wagner instrument for assessing wound severity and the SINBAD (Site, Ischemia, Neuropathy, Bacterial infection, Area ulcer, and Depth) system for improving foot ulcer management according to their severity and predicting major adverse foot events. Results: After three days of treatment using silver dressing, the wound condition shows that the infection has been resolved, and the wound has improved significantly from a grade 2 diabetic ulcer with abscess using the Meggitt-Wagner instrument and a SINBAD score of 3 (less severe ulcer) to a grade 2 diabetic ulcer without abscess and a SINBAD score of 2 (less severe ulcer). Conclusion: Wound care using silver dressing significantly accelerates the resolution of infections and supports the overall healing process

Constructability-driven design of frame structures with state-space search methods

In the design of frames and trusses, the relationship between the structural form, the construction sequence, and the structural behavior during construction is rarely systematically considered. This paper proposes a method to systematically consider constructability in design, specifically focusing on minimizing the maximum displacement or stress during construction. The paper presents the formulation of the optimal assembly sequencing problem as a state-space search with a unique design of the search heuristic and constraint bounding schemes. It proposes three variants of heuristic search algorithms for finding a feasible sequence, a diverse set of feasible sequences, and an optimal sequence, respectively. These algorithms are tested on a case study structure to showcase their ability to navigate the combinatorial space of assembly sequences, and provide means for designers to incorporate sequence-related performance into conceptual structural design.

Optimal allocation of distributed generation on DC Networks

A consistent increase in the installation of distributed generation (DG) in response to rising energy cost and demand throughout the world, especially for the last two decades, primarily aims to obviate upgrading investments for transmission and distribution lines on the existing power grid while ensuring sustainable, resilient, and reliable service to end users. Random installation of DG, on the other hand, can invalidate the existing grid’s designated objectives, and operational boundaries due to the changes in network topology resulting in unforeseen load flow. Appropriate integration of DG units on DC networks should be remedy to high transmission/distribution losses in lines, instabilities in voltage profiles, issues in power quality parameters, and derogation in the protection schemes. Based on the behavior of load profiles, e.g., 24 h or longer period, operational and technical constraints, this study proposes a framework that minimizes transmission/distribution losses on a DC network, in which discrete variables stand for the location of DGs. Optimal power flow (OPF) problem formulation underpins the proposed optimization framework. Overall, this computationally challenging planning problem refers to a mixed-integer nonlinear program (MINLP) including discrete variables with non-convex power balance/flow representations. To deal with the non-convexities and discrete form of the formulation, a mixed-integer second-order cone programming (MISOCP) relaxation, and branch-and-bound search are assigned. The validation of the introduced approach is carried out on the IEEE modified benchmark systems. With the help of proposed method, optimal allocation of DG units on 9-bus, 14-bus, and 30-bus systems, respectively, lead up to 99.93%, 94.38%, and 79.09% total power losses reduction.

Method with batching for stochastic finite-sum variational inequalities in non-Euclidean setting

Variational inequalities are a universal optimization paradigm that incorporate classical minimization and saddle point problems. Nowadays more and more tasks require to consider stochastic formulations of optimization problems. In this paper, we present an analysis of a method that gives optimal convergence estimates for monotone stochastic finite-sum variational inequalities. In contrast to the previous works, our method supports batching, does not lose the oracle complexity optimality and uses an arbitrary Bregman distance to take into account geometry of the problem. Paper provides experimental confirmation to algorithm’s effectiveness.

Application of hybrid chaotic particle swarm optimization and slime mould algorithm to optimally estimate the parameter of fuel cell and solar PV system

Through the use of fuel cells and photovoltaic (PV) solar cells, green energy has become one of the most popular developments in electricity generation in recent years. Accurate modeling of fuel cell and PV cell is critical in the simulation and analysis of energy systems that use both sources. Due to the complexity and nonlinearity of these hybrid systems, the model must be optimized under different operating conditions. In this article, a novel metaheuristic efficient hybrid algorithm is proposed for this purpose. A Hybrid Chaotic Particle Swarm Optimization and Slime Mould Algorithm (HCPSOSMA) has been proposed to estimate the PV/fuel cell system parameters. The hybrid HCPSOSMA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability. The problem formulation is based on minimizing the Root Mean Square Error (RMSE) between the calculated values and measured values for both the triple-diode PV model and the proton exchange membrane (PEM) fuel cell. The results show that the hybrid algorithm outperforms other algorithms in determining the best PV/PEMFC parameters that produce accurate V–I and P–I characteristic curves and the RMSE is about 4 × 10–12. Finally, non-parametric tests based on Friedman's ranking, Wilcoxon's rank sum test and Kruskal-Wallis test are implemented to prove the superiority of the proposed hybrid algorithm in the parameter estimation.

Learning the optimal power flow: Environment design matters

To solve the optimal power flow (OPF) problem, reinforcement learning (RL) emerges as a promising new approach. However, the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment. In this work, we collect and implement diverse environment design decisions from the literature regarding training data, observation space, episode definition, and reward function choice. In an experimental analysis, we show the significant impact of these environment design options on RL-OPF training performance. Further, we derive some first recommendations regarding the choice of these design decisions. The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.

Autonomous Inventions, Problem Formulation, and Inventive Outcomes

Autonomous Inventions, Problem Formulation, and Inventive Outcomes

Continuous-Time Reinforcement Learning Control: A Review of Theoretical Results, Insights on Performance, and Needs for New Designs.

This exposition discusses continuous-time reinforcement learning (CT-RL) for the control of affine nonlinear systems. We review four seminal methods that are the centerpieces of the most recent results on CT-RL control. We survey the theoretical results of the four methods, highlighting their fundamental importance and successes by including discussions on problem formulation, key assumptions, algorithm procedures, and theoretical guarantees. Subsequently, we evaluate the performance of the control designs to provide analyses and insights on the feasibility of these design methods for applications from a control designer's point of view. Through systematic evaluations, we point out when theory diverges from practical controller synthesis. We, furthermore, introduce a new quantitative analytical framework to diagnose the observed discrepancies. Based on the analyses and the insights gained through quantitative evaluations, we point out potential future research directions to unleash the potential of CT-RL control algorithms in addressing the identified challenges.

Towards efficient solutions for vehicle routing problems for oxygen supply chains

This work investigates an integrated production–inventory-routing problem (PIRP) for liquid oxygen supply chains consisting of multiple plants, customers, and heterogeneous vehicles. Solving such a problem is challenging, especially when dealing with large-scale industrial problems. A two-level procedure is adopted that determines decisions regarding production, inventory, and product allocation by simplifying the routing component in the first level. Then, the routing decision is considered in the lower level for which 3-index mathematical programming formulations are presented. To address the combinatorial complexity of the lower-level decision, we propose alternative multi-trip heterogeneous vehicle routing problem (MTHVRP) formulations together with a column generation-based solution strategy. A set of test instances and a real-world case study demonstrate the applicability and performance of the formulations and solution methods.

Can Academic Startups be Broad? How Problem Formulation Shapes Market Breadth Among Startups in AI

Can Academic Startups be Broad? How Problem Formulation Shapes Market Breadth Among Startups in AI

Solution of the plane problem of thermoelasticity with variable properties with a new variant of the derivation of equations in stresses

The interaction of thermal and mechanical fields in modern technologies for the synthesis and processing of materials attracts more and more attention. This is due to the need to predict the behavior and properties of new materials and products depending on the conditions of their creation. In the present work, attention is focused on the comparison of the solutions to the thermoelasticity plane problems with and without accounting for the temperature dependence of the material properties. It is assumed that an external heat source traveling over the surface with a specific velocity and following a given trajectory is responsible for a significant temperature increase. It may correspond to a laser source, for example. The new way of problem formulation in stresses is used. The temperature dependence of the characteristics leads to nonlinear thermoelasticity equations when the equilibrium equations, the requirement of strain compatibility, and the Duhamel-Neuman relation for the plane stress state case are combined. To determine the temperature field, a nonlinear thermal conductivity equation is used. To solve the problem numerically, the use of an implicit finite-difference scheme and coordinate splitting for the thermal conductivity equation and the finite-difference method of successive over-relaxation with Chebyshev acceleration for the equations for stresses was made. As a result, the difference in the solution of linear and nonlinear problems is demonstrated for the considered cases. Using the example of a mixture of titanium and aluminum powders, numerical modeling demonstrated that some stress components can reach values up to 3.5 times higher than those obtained in the case of temperature-independent material properties when there is a 500-degree local temperature increase.

Optimized model involving forward and inverse problem formulation for the assessment of environmentally sustainable transportation alternatives based on intuitionistic linguistic weighted average

Optimized model involving forward and inverse problem formulation for the assessment of environmentally sustainable transportation alternatives based on intuitionistic linguistic weighted average

Remote sensing data fusion approach for estimating forest degradation: a case study of boreal forests damaged by Polygraphus proximus

In the context of global climate change and rising anthropogenic loads, outbreaks of both endemic and invasive pests, pathogens, and diseases pose an increasing threat to the health, resilience, and productivity of natural forests and forest plantations worldwide. The effective management of such threats depends on the opportunity for early-stage action helping to limit the damage expand, which is difficult to implement for large territories. Recognition technologies based on the analysis of Earth observation data are the basis for effective tools for monitoring the spread of degradation processes, supporting pest population control, forest management, and conservation strategies in general. In this study, we present a machine learning-based approach for recognizing damaged forests using open source remote sensing images of Sentinel-2 supported with Google Earth data on the example of bark beetle, Polygraphus proximus Blandford, polygraph. For the algorithm development, we first investigated and annotated images in channels corresponding to natural color perception—red, green, and blue—available at Google Earth. Deep neural networks were applied in two problem formulations: semantic segmentation and detection. As a result of conducted experiments, we developed a model that is effective for a quantitative assessment of the changes in target objects with high accuracy, achieving 84.56% of F1-score, determining the number of damaged trees and estimating the areas occupied by withered stands. The obtained damage masks were further integrated with medium-resolution Sentinel-2 images and achieved 81.26% of accuracy, which opened the opportunity for operational monitoring systems to recognize damaged forests in the region, making the solution both rapid and cost-effective. Additionally, a unique annotated dataset has been collected to recognize forest areas damaged by the polygraph in the region of study.

A finite volume formulation for one-dimensional Stefan problems

This paper presents a numerical formulation that combines the finite-volume method with a time-dependent boundary-fitted coordinate system to tackle the moving boundary problems for the one-dimensional (1-D) heat equation. Following the formulation process, an algorithm was created with the help of the linear algebra package Eigen to generate the data for the numerical solution. Four benchmark cases of heat conduction were investigated by this approach and compared to the analytical solution: a fixed boundary case, the liquid phase of a melting process, the solid phase of a melting process, and an ablating process. The results demonstrate the capability of the presented numerical formulation in efficiently solving the general 1-D heat problems, especially the one-phase Stefan problems, as well as the consistency of the numerical and analytical solutions. Future works can strive to extend the formulations to Stefan problems of two phases or higher dimensions. The work can then be extended to real-life applications, such as the spacecraft re-entry ablation process, and the process of iceberg melting, demonstrating its critical value in solving practical problems.

Simulation-based optimization concept for integrated assessment models: Case study MEDEAS-world

Simulation based optimization concept has been so far presented at local and national energy systems planning levels. This method represents a systematic way of dealing with uncertainties and combinatorial complexity when using modelling tools of very large size with significant number of possible policy inputs, and could be useful to analyse policy recommendations in broader models integrating more dimensions than energy. For the first time, the concept is successfully demonstrated using a working integrated assessment model (IAM), although some attempts have been mentioned in literature previously. IAMs aim to link main features of society, economy and energy with the biosphere and atmosphere into one modelling framework. The concept has been demonstrated for the IAM MEDEAS-World using system dynamics platform Vensim (version 9.3.4.) for both simulation and optimization. For an optimization problem formulation, 12 decision variables are selected from numerous input model parameters, the gross domestic product per capita has been selected for a goal function from simulation result as quantifiable policy objective, while for the constraint CO2 emissions is selected as subject to binding targets. It is possible to compile the simulation code so that optimization running time is around 10 times shorter. Preliminary results show that it is possible to implement richer optimization problems without significant increases in the running time using personal computers. The main contribution of this work is to prove the concept of simulation-based optimization by a demonstration within a working IAM. This opens the way to promising future work to inform about optimal combinations of available policies within different storylines, as well as providing additional general understanding of the IAM which could allow to improve the model. The original Vensim code has been provided in the Appendix.