Programming Structure Research Articles

Structured Chain-of-Thought Prompting for Code Generation

Large Language Models (LLMs) have shown impressive abilities in code generation. Chain-of-Thought (CoT) prompting is the state-of-the-art approach to utilizing LLMs. CoT prompting asks LLMs first to generate CoTs ( i.e., intermediate natural language reasoning steps) and then output the code. However, the accuracy of CoT prompting still can not satisfy practical applications. For example, gpt-3.5-turbo with CoT prompting only achieves 53.29% Pass@1 in HumanEval. In this paper, we propose Structured CoTs (SCoTs) and present a novel prompting technique for code generation named SCoT prompting. Our motivation is that human developers follow structured programming. Developers use three programming structures ( i.e., sequential, branch, and loop) to design and implement structured programs. Thus, we ask LLMs to use three programming structures to generate SCoTs (structured reasoning steps) before outputting the final code. Compared to CoT prompting, SCoT prompting explicitly introduces programming structures and unlocks the structured programming thinking of LLMs. We apply SCoT prompting to two LLMs ( i.e., gpt-4-turbo, gpt-3.5-turbo, and DeepSeek Coder-Instruct-{1.3B, 6.7B, 33B}) and evaluate it on three benchmarks ( i.e., HumanEval, MBPP, and MBCPP). SCoT prompting outperforms CoT prompting by up to 13.79% in Pass@1. SCoT prompting is robust to examples and achieves substantial improvements. The human evaluation also shows human developers prefer programs from SCoT prompting.

A Novel Approximate Dynamic Programming Structure for Optimal Control of Discrete-Time Time-Varying Nonlinear Systems

A Novel Approximate Dynamic Programming Structure for Optimal Control of Discrete-Time Time-Varying Nonlinear Systems

Optimal Dosage and Session Structure of Mindfulness-Based Programming for Youth Across Developmental Periods: Delphi Approach Outcomes

Purpose Research of mindfulness-based programming/interventions (MBP) for youth has proliferated in recent years; however, heterogeneity of MBP for youth impede determination of optimal programmatic structure across developmental periods. Design/Approach/Methods Expert MBP scientists and instructors were iteratively surveyed using the Delphi method to assess optimal MBP session structure across developmental periods. Findings Survey Round 1 defined the participants’ perspective of ideal structure across early childhood, middle childhood, and adolescence. In Survey Rounds 2 and 3, participants considered the preceding Round's results (means and SDs) and indicated what they believed was the ideal structure of a single MBP class. Final results following the Round 3 survey indicated that MBP session period should be progressively longer relative to the developmental time period (early childhood mean = 20.0 min; middle childhood mean = 34.5 min; adolescence mean = 50.0 min); that roughly half of a youth MBP session should be dedicated to mindfulness practice; and that progressively more time should be allocated to didactic instruction and discussion, relative to mindfulness practice, as youth become older. Originality/Value These findings are the first to report expert feedback on the ideal structure of a youth MBP session, and results have significant implications for future youth MBP evaluation, implementation, and curriculum development.

A novel branch‐and‐bound algorithm for solving linear multiplicative programming problems

AbstractThis article proposes a rectangular branch‐and‐bound algorithm for solving linear multiplication problems (LMP) globally. In order to obtain a reliable lower bound of the original problem, this article designs a novel linear relaxation programming problem (LRP) that has not been seen in the existing literature. Based on the basic framework of the rectangular branch and bound algorithm, this article proposes an algorithm that can obtain a global solution. According to the structure of linear relaxation programming, the article designs a region reduction technology to improve the efficiency of the algorithm. This article also provides convergence analysis to ensure the reliability of the algorithm. Finally, several numerical experiments are used to demonstrate the effectiveness and robustness of the algorithm.

Decomposition methods for multi-horizon stochastic programming

Multi-horizon stochastic programming includes short-term and long-term uncertainty in investment planning problems more efficiently than traditional multi-stage stochastic programming. In this paper, we exploit the block separable structure of multi-horizon stochastic linear programming, and establish that it can be decomposed by Benders decomposition and Lagrangean decomposition. In addition, we propose parallel Lagrangean decomposition with primal reduction that, (1) solves the scenario subproblems in parallel, (2) reduces the primal problem by keeping one copy for each scenario group at each stage, and (3) solves the reduced primal problem in parallel. We apply the parallel Lagrangean decomposition with primal reduction, Lagrangean decomposition and Benders decomposition to solve a stochastic energy system investment planning problem. The computational results show that: (a) the Lagrangean type decomposition algorithms have better convergence at the first iterations to Benders decomposition, and (b) parallel Lagrangean decomposition with primal reduction is very efficient for solving multi-horizon stochastic programming problems. Based on the computational results, the choice of algorithms for multi-horizon stochastic programming is discussed.

Optimal Spin Polarization Control for the Spin-Exchange Relaxation-Free System Using Adaptive Dynamic Programming.

This work is the first to solve the 3-D spin polarization control (3DSPC) problem of atomic ensembles, which controls the spin polarization to achieve arbitrary states with the cooperation of multiphysics fields. First, a novel adaptive dynamic programming (ADP) structure is proposed based on the developed multicritic multiaction neural network (MCMANN) structure with nonquadratic performance functions, as a way to solve the multiplayer nonzero-sum game (MP-NZSG) problem in 3DSPC under the constraints of asymmetric saturation inputs. Then, we utilize the MCMANNs to implement the multicritic multiaction ADP (MCMA-ADP) algorithm, whose convergence is proven by the compression mapping principle. Finally, the MCMA-ADP is deployed in the spin-exchange relaxation-free (SERF) system to provide a set of control laws in 3DSPC that fully exploits the multiphysics fields to achieve arbitrary spin polarization states. Numerical simulations support the theoretical results.

Vehicle Selection and Resource Allocation for Federated Learning-Assisted Vehicular Network

To exploit the massive amounts of onboard data in vehicular networks while protecting data privacy and security, federated learning (FL) is regarded as a promising technology to support enormous vehicular applications. Despite that FL has great potential to improve the architecture of intelligent vehicular networks, the mobility of the vehicles and the dynamic nature of wireless channels make the integration of FL and vehicular networks more challenging. In this paper, we propose a vehicle mobility- and channel dynamic-aware FL (MADCA-FL) scheme to fit vehicular networks and enhance learning performances. This novel scheme enables the RSU to select appropriate vehicles and weightedly average the local models. Afterward, MADCA-FL formulates a problem to maximize the model accuracy while assuring the latency and energy restrictions, by jointly optimizing the computation and communication resources. With a mixed- integer non-linear programming structure, the problem is NP-hard. Firstly, we utilize the successive convex approximation algorithm to handle the non-convexity, and then apply the Lagrange multiplier method and the block coordinate descent method to obtain the optimal solution. Extensive experiments are conducted to confirm the effectiveness of our proposed scheme.

Health sciences librarians' instructional engagement in continuing education: a scoping review.

Healthcare professionals (HCPs) have an ongoing need for continuing education (CE) while Health Science Librarians (HSLs), accustomed to supporting a range of learning needs in a variety of contexts, are well situated to provide CE that addresses information retrieval, literacy, management, and more. To better understand the extent of HSL delivered CE activities, we undertook a scoping review to determine how HSLs instruct practicing HCPs in support of their CE. We searched for published and unpublished literature sources including PubMed (NCBI), Embase (Elsevier); Dissertations and Theses Global (ProQuest); CINAHL (EBSCO); Library, Information Science and Technology Abstracts (EBSCO); and Library Literature and Information Science Full Text (EBSCO). To identify unpublished sources, we searched the internet using Google and contacted two health sciences library listservs. We also performed backwards and forwards searching of our included sources. Our database searches yielded 4842 sources, and we retrieved an additional 579 sources through supplementary retrieval methods. After duplicate removal and screening, we included 105 sources in this review. The included sources were published between 1970 to 2021 and covered a range of topics such as searching methods and tools, critical appraisal, and many more. Those related to evidence-based practice (EBP) appeared around 2001 and bibliometrics and bioinformatics arose after 2016. Publications depicting HSLs teaching CE most commonly occurred in academic settings. The most common population taught was nurses, followed by physicians. Most sources did not report using an information literacy framework or instructional design model, undertaking needs assessments, or reporting formal objectives or assessment. While HSLs are active supporters of EBP, we need to apply the same principles to our own professional practice. Formal structure of programming and program assessment combined with clear, detailed reporting can help to build a more robust evidence base to support future CE provision.

Smart landscaping design for sustainable net-zero energy smart cities: Modeling energy hub in digital twin

In our study, we introduce an innovative energy management system optimized for energy hubs in net-zero energy smart cities, emphasizing sustainability as a core objective. Our approach intricately integrates distributed generation systems and storage facilities within a digital twin framework, incorporating landscape considerations to promote environmental harmony. By prioritizing sustainability, our strategy aims to minimize operational costs while maximizing energy efficiency and reducing carbon emissions. We employ specialized energy flow algorithms for each grid, designing energy hubs that leverage renewable resources and mobile storage to enhance sustainability. To address complexity, our model utilizes a non-convex mixed-integer nonlinear programming structure and robust adaptive optimization techniques, combining the Gray Wolf and Krill Herd algorithms. Through simulations, we demonstrate significant improvements in power system efficiency, including notable reductions in power loss, peak voltage fluctuations, and temperature variations. Our research underscores the critical role of smart landscaping in advancing sustainable energy management for net-zero energy smart cities, facilitating seamless integration of energy hubs with the surrounding landscape within a digital twin paradigm.

PVLab: an innovative and flexible simulation tool to better size photovoltaic units

This paper deals with a new highly modular simulation tool, named as “PVLab” and developed by the GREMAN laboratory, to better size photovoltaic (PV) installations. The programming structure and the physical models implemented within this simulation tool are explained. Several case studies are proposed to highlight the relevance of this new simulation tool. The yearly virtual electrical energy production results of grid-connected PV plants are discussed. In particular, these results are compared with the PVsyst tool ones. PVLab has a high level of flexibility, allowing modifying the physical models and databases (for instance, meteorological data) according the users’ needs. This is permitted through the expertise in all the computing steps and particularly, the MATLAB development environment. Controlling the source code gives itself a huge potential in the field of renewable energy applications in comparison with PVsyst which is currently the commercial reference. This point is particularly discussed at the end of the paper.

The Multi-Purpose Robot Controlled via VR

There are still many high-risk industries in society, so it is very important for robots to replace humans to complete these tasks. However, existing robotics are often unable to perform simple operations, limiting their widespread use in society. This study proposes a robot control method based on VR (Virtual Reality) technology, which provides new ideas for solving the difficulties of robot operation through remote control. The production process of this method is mainly divided into two parts: programming and mechanical structure design. In terms of mechanical structure, the project is divided into the body of the robot and the part of the operator. Through this design, the operator can operate the robot remotely. In the end, the project has been verified and achieved good results. By using VR technology to realize remote control of robots, the difficulty of operation is greatly reduced and the application of robots in high-risk industries has been improved. This research provides new ideas and solutions for the application of robotics in high-risk industries.

Vibration Suppression of a Flexible Beam Structure Coupled with Liquid Sloshing via ADP Control Based on FBG Strain Measurement

In this study, an adaptive dynamic programming (ADP) control strategy based on the strain measurement of a fiber Bragg grating (FGB) sensor array is proposed for the vibration suppression of a complicated flexible-sloshing coupled system, which usually exists in aerospace engineering, such as launch vehicles with a large amount of liquid propellant as well as a flexible beam structure. To simplify the flexible-sloshing coupled dynamics model, the equivalent spring-mass-damper (SMD) model of liquid sloshing is employed, and a finite-element method (FEM) dynamic model for the beam structure coupled with the liquid sloshing is mathematically established. Then, a strain-based vibration dynamic model is derived by employing a transformation matrix based on the relationship between displacement and strain of the beam structure. To facilitate the design of a strain-based control, a tracking differentiator is designed to provide the strains’ derivative signals as partial states’ estimations. Feeding the system with the strain measurements and their derivatives’ estimations, an ADP controller with an action-dependent heuristic dynamic programming structure is proposed to suppress the vibration of the flexible-sloshing coupled system, and the corresponding Lyapunov stability of the closed-loop system is theoretically guaranteed. Numerical results show the proposed method can effectively suppress coupled vibration depending on limited strain measurements irrespective of external disturbances.

Two Kadane Algorithms for the Maximum Sum Subarray Problem

The maximum sum subarray problem is to find a contiguous subarray with the largest sum. The history of algorithms to address this problem is recounted, culminating in what is known as Kadane’s algorithm. However, that algorithm is not the algorithm Kadane intended. Nonetheless, the algorithm known as Kadane’s has found many uses, some of which are recounted here. The algorithm Kadane intended is reported here, and compared to the algorithm attributed to Kadane. They are both linear in time, employ just a few words of memory, and use a dynamic programming structure. The results proved here show that these two algorithms differ only in the case of an input consisting of only negative numbers. In that case, the algorithm Kadane intended is more informative than the algorithm attributed to him.

Parameter identification and model prediction path following control of underactuated AUV: Methodology and experimental verification

This paper explores the integrated optimal control scheme from the identification of underactuated autonomous underwater vehicle (AUV) system dynamics model parameters to the application of Model Predictive Control (MPC) technology in underactuated AUV motion control, and presents the simulation and field test results. First, the AUV kinematics and dynamics models are introduced to represent the horizontal plane motion characteristics. Based on Computational Fluid Dynamics (CFD) numerical simulation and overset mesh technology, AUV plane motion is simulated to determine the unknown hydrodynamic coefficients in the model, and the accuracy of the mathematical model is validated by comparing the maneuverability simulation and field test results. After that, an adaptive line-of-sight (ALOS) guiding algorithm with exponentially variable forward distance is utilized in the guidance layer to reconcile the rapidity and stability of tracking. The path following problem is formulated as an MPC optimization problem that takes into account multiple realistic constraints and then converted into a standard convex quadratic programming structure, permitting for online real-time optimization. Simulation and field test results indicate that the control algorithm proposed in this paper provides a more effective control strategy than the traditional LOS+PID controller in tracking effect and robustness.

Unlocking the Potential of Programming Education: Enhancing Conceptual Understanding and Student Engagement with Sphero SPRK Robot

This research investigates the use of the 'Sphero SPRK+' robot as a programming assistant to enhance conceptual understanding and engagement in programming education. The research addresses the challenges of teaching programming and underlines the significance of students understanding programming structures, semantics, syntax and planning. The Sphero robot offers multiple programming options, including graphic blocks, a pictorial command line, and text commands, catering to diverse learning styles. The approach of the study is based on a cognitive learning model, with the Sphero robot providing immediate feedback throughout practising. The experiment incorporates two groups: one using Blocks and the other using JavaScript code with Sphero's assistance. Results suggest that using Sphero SPRK robot enhances programming learning outcomes, particularly for novice learners. Participants express satisfaction with the engaging and practical learning experience, favouring the ease of Blocks for learning programming logic. The study provides significance for educational settings and emphasises the value of robotics and block-based programming in promoting algorithmic thinking and computational skills.

Design and manufacture of a soft robot with dual-interaction in virtual reality

This paper examines the design and fabrication of a soft robot that can connect to a virtual reality environment. This study's primary objective is to utilize these technologies concurrently and demonstrate their applicability in various applications, particularly rehabilitation. Therefore, the process of designing and modeling the soft robot is carried out, and an applied model is created using a 3D printer and silicon material, which is then installed on gloves. Using Unity software, a virtual reality environment is created in which programs, commands, and Arduino processors control the movements of the soft robot, allowing the user to move and pick up an object in a real environment while wearing gloves, and to adjust the amount of pressure and angle of its motion based on the size of each virtual object. During the system evaluation phase, a delay in the performance and reaction time of the soft robot installed on the gloves is observed. This delay is reduced by modifying the programming structure, resulting in optimal system functionality. This capability is used to create proper mobility conditions and rehabilitation for the majority of patients with wrist injuries resulting from strokes and accidents, and it may be effective in accelerating patients' recoveries.

PERANCANGAN APLIKASI E-PARTNERSHIP MENGGUNAKAN METODE AGILE BERBASIS WEB PT. INDUSTRI TELEKOMUNIKASI INDONESIA (PERSERO)

PT. The Indonesian Telecommunications Industry is a provider of products and services based on digital service innovation to answer business needs to facilitate automation in consumer company organizations. Every consumer/potential partner who wants to be part of a partnership at PT. The Indonesian Telecommunications Industry previously had to register as a partner manually using a paper registration form for prospective partners, besides that the required documents were still in the form of paper documents that could be lost at any time. The concept of Agile Software Development was coined by Kent Beck and 16 colleagues by stating that agile software development is a way of building software by doing it and helping others build it at once. Agile software development methods or agile methodology is a set of software development methodologies based on iterative development, where requirements and solutions develop through collaboration between organized teams Based on the stages of analysis of admin and user needs, it can be concluded that there are several things that must be considered and made carefully, including the programming structure, database specifications, and user interface design must be in accordance with The e-Partnership system is made to make it easier for companies to provide information about the procurement of goods or services and other informational information. As for partners of PT. The Indonesian Telecommunication Industry makes it easy to get information about procurement and other information. In addition, this system can make it easier for prospective partners to register to become partners online, because to access the main page of procurement information and other information can only be accessed by partners who already have an e-Partnership application account. Admin dashboard made to make it easier to update, add, and delete procurement information and other information. And the information will be informed through the e-Partnership website to the logged in partner.

Multi-stage robust optimal economic accommodation method for renewable energy considering carbon emission cost

The integration of renewable energy contributes to the target of energy saving and emission reduction, which helps to realize “2030 carbon peak, 2060 carbon neutral” strategy of China. Renewable outputs, on the other hand, exhibit uncertainties, intermittentness, and volatility. To accommodate renewable energy, traditional generator assets such as thermal units must incur higher operating costs. To that end, optimizing power system scheduling requires balancing the benefits and costs of renewable integration. In addition, with the establishment of the carbon trading market, carbon emission cost has become one of the essential costs of power generation. As a result, this paper develops a multi-stage robust optimal economic accommodation model that accounts for both operation and carbon emission costs. Besides, the solution nonanticipativity and robustness can be guaranteed by the established. The optimal economic accommodation model is then equivalently reformed into a single-level mixed integer linear programming (MILP) structure using a multi-stage robust explicit decision method with affine policies. Furthermore, a fast evaluation method for economic accommodation rate is proposed based on the solved affine coefficients. Numerical testing confirms that the proposed method outperforms traditional methods. Furthermore, the proposed method avoids paying 52.93% more cost to increase the accommodation rates by 5.74%, 0.74%, 2.28%, and 1.14% for four farms.

Output‐feedback stochastic model predictive control of chance‐constrained nonlinear systems

AbstractThis study covers the output‐feedback model predictive control (MPC) of nonlinear systems subjected to stochastic disturbances and state chance constraints. The stochastic optimal control problem is solved in a stochastic dynamic programming fashion, and the output‐feedback control is performed with the extended Kalman filter. The information state is summarized as a dynamic Gaussian belief model. Thus, the stochastic Bellman equation is transformed into a deterministic equation using this model. The resulting constrained Bellman equation is solved using the proposed constrained, approximate dynamic programming algorithm. The algorithm is proved to have a Q‐superlinear local convergence rate. Numerical experiments show that the proposed algorithm can attain good control performance and reasonable chance‐constraint satisfaction and is computationally efficient owing to its dynamic programming structure.

Microbiota de la leche humana y su impacto en la salud humana.

Breast milk is a complex biological fluid. Additionally to its nutritional impact, it contains diverse bioactive elements related to early metabolic programming and molecular structures, such as microRNA related to the epigenetic signaling process. Now, we know that human milk is not sterile and contains a significant diversity of microorganisms such as bacteria (bacterioma), viruses (viroma) and fungi (mycobiome), all of which integrate the concept of the human milk microbiota. Apparently the origin of this microbiota is found in the entero-mammary circulation, as well as in the retrograde circulation of the skin of the mammary gland, although it is speculated in what percentage the microbiota of the oral cavity of the infant contributes in a significant way. From a functional point of view, it has been shown the transfer of this microbiota to the infant's digestive tract, which is related to better digestive tolerance, lower frequency and intensity of dysfunctionalities of the brain-intestine-microbiota axis, and improved immunity, among others.