Formulation Of Problem Research Articles

Controllability and minimum energy control problem of two-dimensional continuous-time fractional linear systems

The effectiveness of this paper lies in a new formulation and solution of the minimum energy control problem for the fractional two-dimensional systems described by the Fornasini–Marchesini first models. The problem of controllability and minimum energy control of a general fractional two-dimensional Fornasini–Marchesini model is newly considered by the authors in this work. Necessary and sufficient controllability conditions are then established using the Gramian matrix. To carry out this study, a new test was described and used in order to give the existence conditions of the solution to the minimum energy control problem and the procedures for calculating an input minimising the given performance index. The usefulness and the precision of the proposed procedure is demonstrated by a numerical examples, Darboux equation and metal rolling process model as a practical application.

Innovation of Moringa Leaves as Natural Resources of Ketimang Village, Wonoayu Sidoarjo, into a Health-Beneficial Tea

Education is a critical determinant of successful development across economic, social, and cultural dimensions. In parallel, entrepreneurship plays a significant role in regional advancement, particularly within Micro, Small, and Medium Enterprises (MSMEs). Despite the nutritional benefits of Moringa leaves, there is limited awareness among MSME actors regarding their potential applications in health-focused products. This community service program aims to enhance the competencies of MSME entrepreneurs in Ketimang Village by promoting Moringa leaves as a health-beneficial tea. Implementation involved a series of activities, including a location survey, problem formulation, execution, and monitoring, culminating in the development of a community service journal. The outreach efforts led to a marked increase in public awareness about the health benefits of Moringa leaves, particularly in tea form. This initiative uniquely combines educational elements with practical processing methods for Moringa leaves, fostering innovation among local entrepreneurs. The findings underscore the potential of Moringa leaves as a valuable resource for health promotion and entrepreneurial development, suggesting that similar programs could further empower MSMEs while enhancing community health awareness. Highlights: Increased awareness of Moringa leaves' health benefits among MSME entrepreneurs. Utilized a systematic implementation approach, including surveys and evaluations. Fostered innovation by providing practical methods for processing Moringa into tea. Keywords: Moringa leaves, health benefits, MSMEs, community service, entrepreneurship

PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization

AbstractThis work presents the PULPO (Python‐based user‐defined lifecycle product optimization) framework, developed to efficiently integrate life cycle inventory (LCI) models into life cycle product optimization. Life cycle optimization (LCO), which has found interest in both the process systems engineering and life cycle assessment (LCA) communities, leverages LCA data to go beyond simple assessments of a limited number of alternatives and identify the best possible product systems configuration subject to a manifold of choices, constraints, and objectives. However, typically, aggregated inventories are used to build the optimization problems. Contrary to existing frameworks, PULPO integrates whole LCI databases and user inventories as a backbone for the optimization problem, considering economy‐wide feedback loops between fore‐ and background systems that would otherwise be omitted. The open‐source implementation combines functions from Brightway2 for the manipulation of inventory data and pyomo for the formulation and solution of the optimization problem. The advantages of this approach are demonstrated in a case study focusing on the design of optimal future global green methanol production systems from captured CO2 and electrolytic H2. It is shown that the approach can be used to assess sector‐coupling with multi‐functional processes and prospective background databases that would otherwise be impractical to approach from a standalone LCA perspective. The use of PULPO is particularly appealing when evaluating large‐scale decisions that have a strong impact on socioeconomic systems, resulting in changes in the technosphere on which the background system is based and which is often assumed constant in standard LCO approaches regardless of the decisions taken. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Optimal operation of three-phase unbalanced active distribution system based on semidefinite relaxation and convex-concave procedure

The three-phase distribution optimal power flow (D-OPF) problem has attracted much attention in recent years due to practical requirements for managing distributed energy resources and multi-energy customers in unbalanced active distribution systems. To deal with the strong nonconvexity of the three-phase OPF problem, this paper develops the semidefinite programming (SDP) formulation of three-phase OPF problems considering the mutual coupling in multi-phase networks. Instead of directly dropping the rank-one constraints, a sequential convex optimization algorithm based on the convex-concave procedure (CCP) is proposed for recovering a feasible and optimal solution of three-phase power flow. The proposed iterative algorithm not only has high computational efficiency but also guarantees the optimality when the semidefinite relaxation or second-order cone relaxation method is inexact. Case studies on various IEEE testing distribution systems verify that the proposed algorithm recovers the feasible and optimal power flow solution. It also exhibits a relatively low number of iterations and short solving time in large-scale IEEE testing systems.

Sistem Pendukung Keputusan Berbasis K-Means untuk Evaluasi Keberhasilan Bisnis dan Nilai Perusahaan

Business development is in line with the development of increasingly sophisticated technology. This requires every company to compete and be motivated to increase its value as an indicator of success in managing the company so that investors are interested in investing. This study aims to design a K-means-based Decision Support System with a clustering approach to classify the growth rate of company value. Investment Opportunity Set (IOS) and profitability variables are the leading indicators of increasing company value. The problem formulation is how the design of this K-means-based decision support system can assist in classifying the growth rate of the company's value based on the IOS and profitability variables. This research aims to produce a decision support system that can organize the growth rate of company value using the K-means method. System testing is conducted to evaluate the effectiveness of the applied clustering method, focusing on the accuracy of the results. The weighting of IOS and profitability variables is based on the percentage of positive relationship to firm value, and the ultimate goal is to group companies with different growth rates. As a result, the K-means-based Decision Support System, or "Business Growth Prediction Decision Support System," successfully clustered the growth rate of firm value. With reasonable accuracy, measured using the silhouette coefficient, the calculation results show an overall mean silhouette coefficient of 0.684, close to the maximum value of 1. This result confirms that this decision support system can group companies in the L (Low), M (Medium), and H (High) categories based on the level of value growth, using the IOS and profitability variables as the leading indicators. Thus, this research supports decisions related to company growth strategies using K-means-based decision support systems.

Application of inverse heat transfer to fatigue fracture

Cyclic actuation tends to cause self-heating in the material as the structure experiences fatigue, where the movement and coalescence of defects lead to crack formation, propagation, and eventual fracture. This study explores the modeling aspects of the self-heating phenomenon using a one-dimensional inverse heat problem to analyze heat generation and dissipation to quantify the plastic work rates needed for predicting fatigue life. The approach is based on analyzing the surface thermography obtained using an infrared camera and numerically solving an inverse Fourier heat conduction equation. Formulation of the inverse problem via constrained optimization and method of solution for analyzing the fatigue behavior of CS 1018 flat dog bone specimens subjected to fully reversed bending fatigue are presented. The proposed model demonstrates superior accuracy in predicting the location of maximum heat generation and identifying potential fracture zones compared to traditional methods.

Modeling of artificial intelligence-based respiratory motion prediction in MRI-guided radiotherapy: a review.

The advancement of precision radiotherapy techniques, such as volumetric modulated arc therapy (VMAT), stereotactic body radiotherapy (SBRT), and particle therapy, highlights the importance of radiotherapy in the treatment of cancer, while also posing challenges for respiratory motion management in thoracic and abdominal tumors. MRI-guided radiotherapy (MRIgRT) stands out as state-of-art real-time respiratory motion management approach owing to the non-ionizing radiation nature and superior soft-tissue contrast characteristic of MR imaging. In clinical practice, MR imaging often operates at a frequency of 4Hz, resulting in approximately a 300 ms system latency of MRIgRT. This system latency decreases the accuracy of respiratory motion management in MRIgRT. Artificial intelligence (AI)-based respiratory motion prediction has recently emerged as a promising solution to address the system latency issues in MRIgRT, particularly for advanced contour prediction and volumetric prediction. However, implementing AI-based respiratory motion prediction faces several challenges including the collection of training datasets, the selection of prediction methods, and the formulation of complex contour and volumetric prediction problems. This review presents modeling approaches of AI-based respiratory motion prediction in MRIgRT, and provides recommendations for achieving consistent and generalizable results in this field.

Model Order Reduction of an Ultraweak and Optimally Stable Variational Formulation for Parametrized Reactive Transport Problems

Model Order Reduction of an Ultraweak and Optimally Stable Variational Formulation for Parametrized Reactive Transport Problems

Collection of Foundation Assets That Disbanded Due to Bankruptcy in Company Law

The Foundation is a legal entity to which the rights and obligations provided for by the Legislation are attached. Law Number 28 of 2004 concerning Foundations regulates the establishment of the Foundation's legal entity until the dissolution of the Foundation. One of the reasons for the dissolution of the Foundation was bankruptcy, and after the Foundation was declared bankrupt by the court, the Foundation's assets had to be liquidated by liquidators or curators. In this paper, a problem formulation is put forward, namely how to reserve the assets of a Foundation that was dissolved due to bankruptcy? The result of this research is that if a Foundation disbands, its assets will be liquidated and the remaining assets resulting from liquidation will be handed over to other Foundations that have similar activities to the disbanded Foundation. In the event that the remaining proceeds of liquidation are not handed over to another Foundation which has similar activities, the remaining assets may be handed over to another legal entity which has similarities in activities to the dissolved Foundation, and if this is regulated in the Law concerning that legal entity. And if the two options above are not exercised, the wealth will be handed over to the State and its use will be carried out in accordance with the activities of the disbanded Foundation.

Quantum annealing for nearest neighbour compliance problem

Quantum Computing has emerged as a promising alternative, utilising quantum mechanics for faster computations. This paper explores the nearest neighbour compliance (NNC) Problem in Gate-based Quantum Computers, where quantum gates are constrained to operate on physically adjacent qubits. The NNC problem aims to optimise the insertion of SWAP-gates to ensure compliance with these constraints while minimising their count. This work introduces Quantum Annealing to tackle the NNC problem, proposing two Quadratic Unconstrained Optimisation Problem formulations. The formulations are tested on a contemporary Quantum Annealer, and their performance is compared with previous methods. It shows that the prospect of using Quantum Annealing is promising, however, the current state of the hardware makes that finding the embedding is the limiting factor.

Meningkatkan Hasil Belajar Siswa Pada Materi Aku citra Allah Yang Unik dengan Model Problem Based Learning di Kelas VII SMP Negeri 4 Lahewa

The independent curriculum emphasizes the Pancasila student profile. In this classroom action research, the researcher focuses on the dimension of independence which is based on the importance of forming strong character and mental attitudes in the younger generation, especially in facing complex global challenges. Improving learning outcomes through the PBL model on the material I Am the Unique Image of God. Promises. The Class Action Research Report in this study aims to find out whether student learning achievement has increased after implementing the Problem Based Learning Model in Catholic Religious Education and Character Education Subjects for Students in Class VII of SMPN 4 Lahewa in 2023/2024? The research object is the Problem Based Learning (PBL) Model, the form of research is Classroom Action Research with a cycle model, each cycle consists of four stages including: planning stage, implementation stage, observation stage and reflection stage. The data analysis technique used consists of data reduction by selecting raw data into information, data presentation, namely presenting data in the form of descriptions, graphs, tables and drawing conclusions by making conclusions from research results that refer to the problem formulation. The use of the Problem Based Learning model according to the syntax which includes orienting students to problems, organizing students to learn, guiding individual and group investigations, developing and presenting results, analyzing and evaluating the problem solving process, is effective in helping increase learning motivation and student independence in material I Am the Unique Image of God in class VIII of SMPN 4 Lahewa The data collection technique used by researchers was to observe students' activities guided by the P3 rubric. The results of observations from the three participants from the five students in cycle 1 averaged 74% and in cycle II 88% while the learning outcomes were in cycle! I the average was 81.5%, and in cycle II it increased to an average of 86.2%. The increase was seen to be 14% higher than before.

Meningkatkan Hasil Belajar Pendidikan Agama Katolik Bagi Siswa Kelas VII SMPN 3 Kelam Permai Melalui Materi Peran Keluarga Bagi Perkembanganku dengan Metode Problem Based Learning Tahun Ajaran 2024/2025

The conditions observed during Catholic Religion lessons for seventh-grade students at SMPN 3 Kelam Permai in the 2024/2025 academic year indicate a lack of concentration among students. They frequently engage in conversations with peers, exhibit limited fluency in articulating their opinions, struggle with analyzing the material being studied, and demonstrate minimal participation in class activities. These factors collectively contribute to suboptimal student learning outcomes. The problem formulation in this study is as follows: How is the Problem-Based Learning (PBL) method applied to the topic "The Role of the Family in My Development" in class VII at SMPN 3 Kelam Permai? Can the implementation of the Problem-Based Learning (PBL) method enhance the learning outcomes of class VII students at SMPN 3 Kelam Permai in the subject of Catholic Religious Education? Additionally, what challenges are encountered in the application of the Problem-Based Learning (PBL) method in teaching Catholic Religious Education in class VII at SMPN 3 Kelam Permai? The research data gathered by the researcher originates from two primary sources: First, the outcomes of observations regarding the Independence of Interest and Motivation of students, conducted by colleagues or derived from the researcher’s own observations during the instructional unit "My Abilities and Limitations," utilizing the Problem Based Learning (PBL) model in class VII at SMPN 3 Kelam Permai during the 2024/2025 Academic Year. Second, the data collection method for assessing the Independence of Interest and Motivation of students involves administering tests at the conclusion of each cycle. Since the pre-cycle research, Cycle I, and Cycle II, students have consistently surpassed the minimum achievement threshold of 70%. In the pre-cycle research, no students fell within the range of 81-100. In Cycle I, four students (21%) achieved scores in the range of 81-100. In Cycle II, nine students (49%) attained scores within the same range.

Meningkatkan Hasil Belajar Siswa dengan Bantuan Visualisasi Video dan Gambar Menggunakan Model Pembelajaran Problem Based Learning di SMP Swasta Cinta Rakyat 1 Pematangsiantar

Education plays an important role in shaping students' character and competencies, especially in the era of globalization which requires critical and creative thinking skills. At Cinta Rakyat 1 Junior High School, Pematangsiantar City, especially class IX-A, student learning outcomes in several subjects still need to be improved. Initial observations show that many students have difficulty understanding abstract concepts conveyed verbally without the support of visual media, as well as a decrease in learning motivation due to monotonous conventional teaching methods. The use of visual media in the form of videos and images is expected to help students understand complex concepts more easily and improve memory, thereby improving learning evaluation results and increasing active student involvement. This research focuses on improving student learning outcomes in Catholic Religious Education and Character Education subjects in class IX-A by using visual media as a tool in the learning process. The research subjects were all 32 students in class IX-A. Improved learning outcomes are measured through formative assessments in the form of written tests and observations of student activity and involvement during learning. The research problem formulation includes the effectiveness of using visual media in improving learning outcomes, motivation, student involvement, and understanding of abstract concepts. The research results are expected to make a significant contribution in improving the quality of learning through a visual approach that is relevant to student needs.

Scheduling and assessment of multi-area virtual power plant including flexible resources using swarm intelligence technique

A virtual power plant (VPP) makes the distributed energy resources (DERs) visible to support grid services, promoting clean energy use by offering an alternative to reduce reliance on conventional sources. This paper proposes optimized operation and scheduling of VPP by forming a group of flexible resources that includes renewables i.e., solar, wind power, fuel cell, and co-generation units along with storage facilities to form a holistic network in a multi-area interconnected system framework. Initially, the scheduling is performed for a single area system in which a comparison is made with the available work relevant to the proposed formulation. In addition, the scheduling is further extended to the proposed multi-area based virtual power plant (MAVPP) performed on a day-ahead (DA) basis and the target objective(s) i.e., net profit and emission are optimized. Furthermore, a recently developed marine predator optimization algorithm (MPOA) is employed to solve the proposed problem formulation and the results are compared and discussed extensively with conventional techniques. Also, the analysis pertaining to MAVPP is carried out which is beneficial in terms of economic and environmental perspective. The result reveals that the maximum value of net profit for MAVPP is converging to an optimum value of 59,351.88 $ followed by the attained value of minimum emission of 51,700.73 Kg by consuming an elapsed time of 92.96 and 104.76 s., respectively in 200 iterations. The outcomes are promising and upon comparison with the available literature work, the superiority of the selected technique is evident in terms of computational time, improvement of net profit and reduction in emission followed by the ease of convergence behaviour in fewer iterations.

Integer and fractional-order model matching controller design algorithm with generalized reference model formulation for nonlinear time delay servo and regulatory problems: A real-time validation

Integer and fractional-order model matching controller design algorithm with generalized reference model formulation for nonlinear time delay servo and regulatory problems: A real-time validation

The Effect of Scramble Model on Student Activeness of Class VIII SMP Negeri 1 Pademawu

This research is motivated by the fact that student activity is quite crucial in the learning process which ultimately has an impact on increasing their learning activity. So, in order to increase students' active learning, social studies teachers can use models that are interesting and appropriate to students' needs. The aim of this research is to find out whether the scramble model has an effect on student activity. There are 2 problem formulations that are studied in this research, 1) What is the influence of the scramble learning model on student activity in class VIII social studies at SMP Negeri 1 Pademawu 2) What are the supporting and inhibiting factors for using the scramble model on student activity in social studies subjects class VIII at SMP Negeri 1 Pademawu. This research uses quantitative descriptive research methods with simple linear regression data analysis techniques used in this research is simple linear regression. The research results show that the scramble learning model can increase student activity in teaching and learning activities. This is shown by the results of a simple linear regression analysis test with a sig value smaller than the alpha value, namely 0.00 < 0.05, which means the scramble model has a positive and significant effect on student activity. influenced by the results of the coefficient of determination test which produced an influence value of 56% on student activity at SMP Negeri 1 Pademawu, while the rest was influenced by 44%. Next, it can be seen that the results of the calculated t are greater than the t table, namely 9.582 > 1.993, meaning that H0 is rejected and Ha is accepted. So this shows that the scramble.

Revisiting the fitting of the Nelson–Siegel and Svensson models

The Nelson–Siegel and the Svensson models are two widely used models for the term structure of interest rates. These models are quite simple and intuitive, but fitting them to market data is numerically challenging and various difficulties have been reported. In this paper, we provide a novel mathematical analysis of the fitting problem based on parametric optimization. We formulate the fitting problem as a separable nonlinear least-squares problem, in which the linear parameters can be eliminated. We provide a thorough discussion on the conditioning of the inner part of the reformulated problem and show that many of the reported difficulties encountered when solving it are inherent to the problem formulation itself and cannot be tackled by choosing a particular optimization algorithm. Our stability analysis provides novel insights that we use to show that some of the ill-conditioning can be avoided, and that a suitably chosen penalty approach can be used to address the remaining ill-conditioning. Numerical results indicate that this approach has the expected impact while being independent of any choice of a particular optimization algorithm. We further establish smoothness properties of the reduced objective function, putting global optimization methods for the reduced problem on a sound mathematical basis.

Novel mathematical formulations for parallel-batching processing machine scheduling problems

We study mathematical formulations for batch-processing machine scheduling problems (BPMPs), which are the challenging issues in the machine scheduling literature where machines are capable of processing a batch of jobs simultaneously if jobs with non-identical sizes can be packed in a capacitated machine. In this paper, we tackle single- and parallel-machine BPMPs, and other interesting problem variants that aim at minimizing the makespan. We develop novel formulations along with valid inequalities and an algorithm framework that makes use of dual information and bounding techniques to achieve efficiency when instances are intractable. Extensive computational experiments on benchmark instances show that our approaches achieve state-of-the-art results and prove the optimality of intractable instances in the literature.

Research of Converter Station Intelligent Inspection System Algorithm

Abstract This article mainly reveals a converter station equipment defect detection system based on the YOLOv5 algorithm. The research encompasses the establishment of a simulated environment for converter station inspection tasks, problem formulation, algorithm design, training process, and experimental evaluation. Key contributions include the enhancement of the YOLOv5 algorithm through improved clustering techniques, dataset preparation, and multi-object detection model training. This highlights the importance of appropriate anchor box initialization in object detection tasks, especially when dealing with small-sized images and varied defect types. In order to verify the correctness of this research, a comparative simulation analysis with mainstream detection algorithms further validates the superiority of the proposed method. The findings of this research contribute valuable insights into the development of AI-driven defect detection systems for power equipment inspection.

Comment on 'A PET reconstruction formulation that enforces non-negativity in projection space for bias reduction in Y-90 imaging'.

We read with great interest the paper by Limet al(2018Phys. Med. Biol.63035042) on bias reduction in Y-90 PET imaging. In particular, they proposed a new formulation of the tomographic reconstruction problem that enforces non-negativity in projection space as opposed to image space. We comment on the algorithm they derived from this formulation and bring some clarifications on the constraint that this algorithm respects.