Programming Problem Research Articles

Two‐stage stochastic energy and reserve market clearing model considering real‐time offering strategy of renewable energy

AbstractWith the introduction of decarbonization policies worldwide, renewable energy, especially wind and solar power, is gradually taking a significant share in the power system. The two‐stage stochastic optimization methods are generally adopted to address the uncertainty issues and the two‐stage stochastic market clearing models are established with the participation of renewable energy. However, most existing two‐stage stochastic market clearing models usually intuitively presume the offering quantities of renewable energy are equal to their forecast values, neglecting the potential impacts of the strategic offerings on the market results and thus affecting the secure operation of power systems. To fill the gaps, this paper incorporates the strategic offering models of renewable energy into the two‐stage stochastic energy and reserve market clearing model. Within the two‐stage framework, the first day‐ahead stage serves for energy and reserve market clearing, while the second real‐time stage considers the adjustments on offering quantities of renewable energy according to the fluctuations of renewable generations. The two‐stage stochastic market clearing model is formulated as a mixed‐integer non‐linear programming problem, and an iterative algorithm is devised to obtain market clearing results after proper linearizations. The effectiveness of the proposed method is validated using the extended IEEE‐118 test system.

Joint channel allocation and transmit power control for underlay EH‐CRNs: A clustering‐based multi‐agent DDPG approach

AbstractTo address the concerns of energy supply and spectrum scarcity for wireless devices, energy harvesting cognitive radio networks have been proposed. To improve spectrum utilization, secondary users (SUs) access the licensed spectrum in underlay mode, which may cause severe interference to primary users and SUs. The focus is on the underlay energy harvesting cognitive radio networks with multiple pairs of SUs, and formulate the long‐term secondary throughput maximization problem as a mixed‐integer non‐linear programming problem. As traditional approaches could hardly solve the mixed‐integer non‐linear programming problem well, a centralized deep deterministic policy gradient (C‐DDPG) approach is proposed that achieves satisfactory throughput performance. To reduce the computational complexity of C‐DDPG, we further propose a clustering‐based multi‐agent DDPG (CMA‐DDPG) approach that combines the advantages of the centralized deep reinforcement learning approach and the distributed deep reinforcement learning approach. In the CMA‐DDPG, a novel interference‐based clustering algorithm is proposed, which partitions the SUs that cause severe mutual interference into one cluster, and the sizes of state space and action space are smaller than those in C‐DDPG. Numerical results validate the superiority of the proposed approaches in terms of the throughput and outage probability, and validate the clustering performance of the interference‐based clustering algorithm in terms of the outage probability of the secondary network.

How Do First-Year Engineering Students’ Emotions Change while Working on Programming Problems?

Emotions are a complex multi-faceted phenomenon. To assess the complexity of emotions from different facets, multi-modal approaches are necessary. However, multi-modal approaches are rarely used for assessing emotions, especially in the context of computer programming. This study adopts a multi-modal approach to understand the changes in students’ perception of emotions before and after working on programming problems. Understanding these changes in students’ perceptions may enable educators to devise interventions that help students adjust their perceptions and regulate their emotions as per their skills. We conducted a one-on-one programming session and retrospective think-aloud interview with 17 students from an introductory programming course. During the programming session, students filled surveys and performed four programming tasks. While working on these tasks, students’ eye gaze, video of face and screen, and electrodermal activity data were also collected using a non-invasive device. The data collection for this study was multi-modal, with a mix of both qualitative and quantitative data collection methods. Data analysis was primarily qualitative, with additional triangulation of qualitative and biometric data for select exemplars. The findings of this study suggest that students experience changes in emotions because of many reasons, for instance, they encountered repeated errors, they set high standards for their performance, or they could not manage time. For some students, negative emotions changed to positive emotions when they solved errors without any external help or achieved more than what they expected going into the task. Moreover, the triangulation of qualitative and biometric data of two participants provides a fine-grained analysis of their emotions and behaviors and confirmed the change in the perception of their emotions while performing the programming tasks.

SISTEM INFORMASI PENJUALAN CAT BERBASIS MULTIUSER

Growth of computer technology which is fast to progressively give amenities for human being in executing life activity, this matter result usage of computer is alternative taken by various institution because assessed more giving advantages compared to existing loss. Computer are appliance assist to improve performance various area. In finishing the problem of data processing in CV. Karunia Abadi, pursuant to execution of [done/conducted] survey, writer try to alter data-processing program by manual with computerization program.. This change aim gives freshment in the running of him. With computer program in data input still need time old ones and if for the seeking of data also still have to write one by one since it cannot by automatic and presentation which must be sent to leader still walk tardy so that cannot be presented anytime,because of this existence problem the writer try to formulate problems that is " How to make data-processing program of sale by cash and automatically can give report at any times at] leader in CV.Karunia Abadi using program of Microsoft Fox Pros 9.0". For data-processing process of sale can be accessed swiftly, and precisely and also give more drawing appearance. In finishing the problems [of] writer try to collect data - data which is connect with cash sale data. In scheme of system which is just [done/conducted] [by] some steps cover scheme of data. Making of tables, making of ERD. In data processing of the sale expected can assist problems of arising out in CV. Karunia Abadi. Endless Grant from above and made program it is of course still has some insuffiency and weakness, this matter is enabled [by] because still a few/little knowledge and experience of writer in problem of program. Suggestion and criticism very assist writer to be more develop knowledge in the field of this

Piano cycles of Italian composers of the late 19th – first half of the 20th century: on the problem of programming.

The object of study in this article is the piano work of Italian composers of the late 19th – first half of the 20th centuries. The subject is programming in instrumental music, represented by the genre of the piano cycle. Based on the definition of programming and the classification of types, developed in the works of domestic musicologists M. Aranovsky, L. Kazantseva, Yu. Khokhlova, the author reveals the features of its implementation in the cycles of famous Italian composers F. Busoni, J. Malipiero, A. Casella, M. Castelnuovo-Tedesco, created during the historical period under study. Particular attention is paid to considering the issue of continuing the tradition of embodying programming in cycles, established by romantic composers, as well as identifying national features characteristic of Italian art as a whole. The article uses an integrated approach, including systemic-historical and comparative methods of analysis. The author also turned to the method of musical theoretical analysis. The novelty of the research lies in the development of one of the important and relevant areas of musicology associated with the synthesis of arts, within the framework of solving a specific problem: the implementation of various types of programming. The author’s special contribution to the development of this topic is the introduction into scientific circulation of new works of Italian masters that have not previously been studied, the consideration, using their example, of various types of programming in close connection with the tradition that has developed in European art, and the identification of ways to update it. The most important feature is the integrity of the cycles, which is formed at the emotional, figurative, stylistic, compositional, and tonal levels. A conclusion is drawn about the originality of the piano cycles of Italian composers, which is manifested in close connection with national art.

Ecological Approach and Departure‐Driving Strategy Optimized by Using Syncretic Learning with Trapezoidal Collocation Algorithm for the Plug‐In Hybrid Electric Vehicles

The eco‐driving strategy is of great significance in driving cost for plug‐in hybrid electric vehicles in driving trips, especially at signalized intersections. To address the issue of further energy saving, this study proposes an ecological approach and departure‐driving strategy by using syncretic learning with trapezoidal collocation algorithm. First, a syncretic learning‐based speed predictor is built by merging back propagation neural networks and radial basis function neural networks. Second, the syncretic learning‐based speed predictor and trapezoidal collocation algorithm are combined to optimize the speed trajectory. Third, the torque between the engine and the motor is distributed by the dynamic programming algorithm. Then, model predictive control optimizes torque output in the control time domain. Finally, the driving interval optimization method is designed to avoid mixed‐integer programming problems and redundant constraints, which make vehicles cross intersections without stopping. The numerical verification results show that the trapezoidal collocation algorithm with syncretic learning has more advantages than other methods in speed trajectory planning. Compared with the original trajectory, the driving time through the intersection is reduced and the total driving cost is lowered by 19.82%. Validation results confirm the effectiveness of the proposed strategy in energy consumption management at signalized intersections.

Upaya Peningkatan Perilaku Masyarakat melalui Edukasi HIV dan AIDS di Puskesmas Putri Ayu Kota Jambi

HIV/AIDS is one of the endemic diseases in Indonesia, in HIV/AIDS Community, Yayasan Kanti Sehati Sejati Jambi City recorded 1,421 ODHA in Jambi Province. A total of 1,135 (80%) were in Jambi City (71.63% male). This service activity is carried out at the Puskesmas Putri Ay Jambi City, where cases of HIV/AIDS always increase every year. In 2021 there were 70 HIV/AIDS cases (2 people died due to complications of pulmonary tuberculosis). This figure is higher than in previous years, 2018 (27 cases), 2019 (40 cases), 2020 (52 cases). Based on problem identification, there are 2 partner problems, namely program problems and community problems. The purpose of the education implementation is to increase the knowledge and attitudes of participants after attending education about HIV/AIDS. Educational activities are carried out in the form of group education, in 1 meeting for ± 60 minutes in the hall Puskesmas Putri Ayu. Before delivering the material, leaflets were distributed to each participant. Furthermore, the material was delivered through power point using lecture, discussion, question and answer methods and sharing experiences. Before and after education, pretest and posttest were carried out. The results obtained were that there were 11 participants who attended the education and 1 of them was ODHA; teenagers, most of the women who work as housewives without income with the most education are elementary school. There was an increase in the average knowledge of 1.5 and an increase in the average attitude of 2.1. Through this activity, it is hoped that the behavior of the community will improve so that the number of people living with HI/AIDS (ODHA) is reduced

Convex optimization approach to design sensor networks using information theoretic measures

AbstractAccurate and precise estimation of process variables is key to effective process monitoring. The estimation accuracy depends on the choice of the sensor network. Therefore, the article aims at developing convex optimization formulations for designing the optimal sensor network using information‐theoretic measures in linear steady‐state data reconciliation. To this end, the estimation errors are characterized by a multivariate Gaussian distribution, and thus the analytical form for entropy and Kullback–Leibler divergences (forward, reverse, and symmetric) of estimation errors can be obtained to formulate the optimal sensor network design. The proposed information theoretic‐based optimal sensor selection problems are shown to be integer semi‐definite programming problems where the relaxation of binary decision variables results in solving a convex optimization problem. Thus, we use a branch and bound method to obtain a globally optimal sensor network design. Demonstrative case studies are presented to illustrate the efficacy of the proposed optimal sensor selection formulations.

DBSCAN-based line density clustering algorithm for CAD architectural drawings

In the traditional architecture industry, architects often need to convert CAD drawings into BIM in order to express the final effect of the building more concretely and to understand whether the design of each profession is reasonable after the drawings are completed. The whole modeling process is boring and tedious, and due to human fatigue, the final result is prone to problems, which eventually leads to failure to meet expectations. In order to free architects from the tedious task of model transformation, companies have designed software for automatic model transformation using computers. The core of the software design is related to computational geometry. Line segment clustering is one of the elements of computational geometry and a frequent problem in programming. Appropriate clustering of line segments can often bring convenience to the problem. This paper combines the basic idea of the DBSCAN algorithm, improves the shortcomings of DBSCAN algorithm in dealing with line segment clustering, and proposes a density-based line clustering algorithm with shapely library as a tool. The algorithm is highly interpretable, and the method performs well and efficiently in the test of actual drawings, whether to group the line segments or to find the target line segments that meet the conditions, which provides convenience for the subsequent calculation.

The Impact of Using Smart Infusion Pumps among Nursing Practice

The most prevalent cause of medical injuries is medication errors especially when patients take multiple medications at the same period of time with different doses. One of the technologies that have been developed to improve patient outcomes is the smart infusion pump, which has the ability to identify several programming problems when applied correctly. However, based on existing use, there are contradictory reports on smart pumps' potential to avoid patients' harm in the absence of other features, as well as deliver professional care to patients. This study aimed to understand the experience of nurses when performing smart infusion pumps in their practice, the influence of this technology on their confidence, and how much this technology is trusted to rely on to prevent medical errors. Previous studies focused entirely on longitudinal and quantitative studies, which take a long time and cost a lot. A qualitative study collected from an online survey that contained three types of questions: open-ended questions, closed-ended questions, and rating questions to evaluate participants' answers about smart infusion pumps among their practices in Saudi Arabia hospitals, a survey targeting nursing staff and we got 30 replies. The outcomes have revealed a significant impact of smart infusion pumps on nursing practice.

Bundle Enrichment Method for Nonsmooth Difference of Convex Programming Problems

The Bundle Enrichment Method (BEM-DC) is introduced for solving nonsmooth difference of convex (DC) programming problems. The novelty of the method consists of the dynamic management of the bundle. More specifically, a DC model, being the difference of two convex piecewise affine functions, is formulated. The (global) minimization of the model is tackled by solving a set of convex problems whose cardinality depends on the number of linearizations adopted to approximate the second DC component function. The new bundle management policy distributes the information coming from previous iterations to separately model the DC components of the objective function. Such a distribution is driven by the sign of linearization errors. If the displacement suggested by the model minimization provides no sufficient decrease of the objective function, then the temporary enrichment of the cutting plane approximation of just the first DC component function takes place until either the termination of the algorithm is certified or a sufficient decrease is achieved. The convergence of the BEM-DC method is studied, and computational results on a set of academic test problems with nonsmooth DC objective functions are provided.

Optimal strictly stealthy attacks in cyber-physical systems with multiple channels under the energy constraint

This work considers an innovation-based attack policy in cyber-physical systems with multiple channels. The linear deception attack policy only allows the partial channels to be compromised at each step due to the energy constraint in multiple channels. Compared to the existing results with the single channel, the attack matrices and the positions are time-varying in this work. For the multi-channel systems under the energy constraint, the attack policy is designed to determine the optimal attack locations to degrade the estimation performance of the system to the most extent. Then, it is proved that achieving the optimal attack policy is equivalent to solving a limited optimisation problem and a 0–1 programming problem, respectively. Furthermore, simulated examples are presented to illustrate the developed results.

How do students feel and collaborate during programming activities in the productive failure paradigm?

ABSTRACT Background and Context Productive failure (PF) is a learning paradigm that flips the order of instruction: students work on a problem, then receive a lesson. PF increases learning, but less is known about student emotions and collaboration during PF, particularly in a computer science context. Objective To provide insight on students’ emotions and reasoning during PF activities and their relation to performance. Method We conducted a PF study (N = 48) with a programming problem. We used a mixed-methods approach to analyze students’ emotions, reasoning, and pretest/posttest performance. Findings Uncertainty occurred frequently compared to confusion, frustration, and positive affect. The constructive contributions made during problem solving correlated positively with posttest scores. Implications Although students failed to produce a correct solution, there were few instances of frustration and a promising amount of constructive reasoning during collaboration. This, coupled with prior work showing that PF improves learning over standard instruction, indicates that PF is a promising approach for computer science education.

A System Optimization Approach for Trains’ Operation Plan with a Time Flexible Pricing Strategy for High-Speed Rail Corridors

Optimizing the train plan for high-speed rail systems should consider both the passengers’ demands and enterprise’s benefits. The choice of the departure time period is the most important factor affecting the passenger demand distribution. In this paper, the optimization problem of a train operation plan based on time period preference is studied for a high-speed rail corridor. First, according to the travel process of the passengers, the extended service network for a high-speed rail system is established. The main factors that influence the passengers’ travel choices are analyzed, and the departure time period preference, stop time and flexible pricing strategy based on the time period preference are put forward. The generalized travel cost function, including the convenience, ticket fare and stop time costs, is constructed, and a two-level programming model is established based on the function. The upper-level planning model is formulated as a mixed 0–1 programming problem that aims at maximizing the revenue of the railway enterprise. It is mainly constrained by passenger travel demand and solved by improved genetic algorithms. The lower-level model is a user equilibrium (UE) model. The Frank–Wolfe algorithm is used to allocate multiple groups of OD (origin and destination) passenger flows to each train so that the generalized travel expenses of all the passengers with the same OD are minimized and equal. Finally, the train operation plan is solved based on the Lan-xi (Lanzhou–Xi’an) high-speed rail data, and the validity of both the model and algorithm is verified.

EXAMPLES OF INNOVATIVE METHODS OF TEACHING COMPUTER GRAPHICS SUBJECTS (RESULTS AND DISCUSSION)

This article presents examples, results, and discussion of innovative methods for teaching computer graphics topics. The article examines teaching methods in the field of computer graphics and offers reflections on their innovative approaches. The article begins with the importance of virtual developments in the teaching of computer graphics subjects. Virtual technologies, such as Virtual Reality (VR) and Augmented Reality (AR), allow providing interactive and immersive ways to study computer graphics. Examples show results and discussions of VR in teaching for different fields. In the next part, the article reviews the available techniques of interactive teaching. For example, in interactive graphic design classes, students participate in the creation process of computer graphics programs and work with them to solve basic problems in programming and creation. This method helps you get results according to the schedule and guided counseling during the student creation process. In the next part of the article, innovative aspects of collaborative learning are presented. Examples show how to develop computer graphics projects collaboratively and ensure better learning outcomes through pooled resources. In doing so, students can use them to manage their work, enjoy it, and evaluate their work. In the last part of the article, attention is paid to the presentation of innovative methods of distance education in the field of computer graphics. Distance learning enables students to learn through video conferencing, webinars, and online platforms. This method is a modern way of learning computer graphics and allows students to learn with a constant volunteer.

Focus programming: a bi‐level programming approach to static stochastic optimization problems

AbstractStatic stochastic optimization problems are formulated with the focus theory of choice where the optimal solution is determined as per which solution's focus (the most salient realization of a random vector) is the most preferred. The new formulation that we call the focus programming is a bi‐level programming approach in which the lower‐level program is used to choose the focus of each feasible solution and the upper‐level program is to determine the optimal solution. Since in focus programming models upper‐level and lower‐level programs are maximin or minimax problems, they are nonsmooth and sometimes even nonconvex so that the existing optimization methods cannot solve such bi‐level programming problems. We propose several single‐level reformulation methods for such problems.

Resource Allocation for 5G Network Considering Privacy Protection in Edge Computing Environment

In the field of Internet of Things, mobile communication will be deeply integrated with industrial construction, biomedicine, agricultural production, transportation and other industries to fully realize the “Internet of Everything” and realize smart city and sustainable development. This paper intends to solve problems in increasing latency and consumption of energy and security after deploying Mobile Edge Computing (MEC) servers in single cell multi-user network model, and has advocated a strategy of allocating the resources of 5G communication in regards to the protection mechanism for privacy in the environment of edge computing. First of all, solution model for the problem is characterized as a model with time delay and energy consumption. Second, privacy is used to measure the uncertainty of data in order to quantify privacy data while computing the allocation of tasks and facilitate the design of objective function. Finally, the task migration problem of minimizing energy consumption under the limitation of completion time is constructed as a nonlinear 0–1 programming problem. Besides, we design the optimal resource allocation strategy of discrete algorithm of binary particle swarm optimization (BPSO) for problem solution. Results indicate that while the quantity of users is 100, the total user expenditure of proposed method is 18.3J, which is lower than 20.0J and 30.7J of the compared methods. Moreover, the proposed method has lower latency and energy consumption, which can well balance the load of MEC servers.

New Mixed-Integer Nonlinear Programming Formulations for the Unit Commitment Problems with Ramping Constraints

Mixed-Integer Formulations for Power Production Problems The unit commitment problem is a complex mixed-integer nonlinear program that originates in the field of power production. Although it arises in a monopolistic system, there is still great attention to this problem even in a free-market regime, where it constitutes only a subproblem of larger ones. Historically, it was usually solved by Lagrangian relaxation methods. However, the advances achieved by commercial solvers of mixed-integer (linear and convex) programming problems have made such approaches an attractive option. T. Bacci, A. Frangioni, C. Gentile, and K. Tavlaridis-Gyparakis present the first mixed-integer nonlinear programming formulation with a polynomial number of both variables and constraints that describes the convex hull of the feasible solutions of the unit commitment problem with a single thermal generation unit, comprising all typical constraints and convex power generation costs. Proving that the formulation is exact requires a new result about the convex envelope of specially structured functions that can have independent interest. This new formulation for a single power generation unit is used to derive three new formulations for the general unit commitment problem whose effectiveness has been tested against the state-of-art formulation.

A Mapping Method Tolerating SAF and Variation for Memristor Crossbar Array Based Neural Network Inference on Edge Devices

There is an increasing demand for running neural network inference on edge devices. Memristor crossbar array (MCA) based accelerators can be used to accelerate neural networks on edge devices. However, reliability issues in memristors, such as stuck-at faults (SAF) and variations, lead to weight deviation of neural networks and therefore have a severe influence on inference accuracy. In this work, we focus on the reliability issues in memristors for edge devices. We formulate the reliability problem as a 0–1 programming problem, based on the analysis of sum weight variation (SWV) . In order to solve the problem, we simplify the problem with an approximation - different columns have the same weights, based on our observation of the weight distribution. Then we propose an effective mapping method to solve the simplified problem. We evaluate our proposed method with two neural network applications on two datasets. The experimental results on the classification application show that our proposed method can recover 95% accuracy considering SAF defects and can increase by up to 60% accuracy with variation σ =0.4. The results of the neural rendering application show that our proposed method can prevent render quality reduction.

A Structural, Time Aware, Coordinated Tag Generation Based on Transformer Network

The content quality of shared knowledge in Stack Overflow (SO) is critical in supporting software developers with their programming problems. Thus, it allows its users to suggest editing the software to improve the quality of a post. However, existing all research shows that many suggested edits in SO are rejected due to undesired contents or violating editing guidelines. Such a scenario frustrates or demotivates users who would like to conduct good-quality edits. we propose Semantically Tag and Score Recommendation, with the use of the deep learning-based approach that automatically recommends tags or grades or scores through learning the semantics of both tags, score, grade and questions in such software CQA. First, word embedding is employed to convert text information to high-dimension vectors for better representing questions and tags. Second, a Multitasking, the core modules of Semantically Tag and Score Recommendation, is designed to capture short and long semantics. Third, the learned semantic vectors are fed into a gradient descent-based algorithm for classification