Code Optimization Research Articles

Blockchain-enabled framework for transactive home energy management with cloud energy storage under uncertainties

In modern paradigm of smart grid, prosumers can trade energy peer-to-peer in a transactive energy market to reduce energy costs and improve energy efficiency. In addition, cloud energy storage (CES) is a type of shared energy storage systems with high economic efficiency that can provide energy storage services for prosumers and become an active player in energy trading. However, transactive energy implementation in power systems has several challenges such as data privacy and security. In existing researches, energy trading mechanisms rely on central authorities, where security and privacy cannot be maintained. Therefore, this paper presents a decentralized stochastic optimization model of transactive energy management based on blockchain in the presence of CES. In this framework, the smart contract based on Alternating Direction Method of Multipliers technique is deployed on the Ganache and Ethereum blockchains, and the YALMIP toolbox along with CPLEX solver handle decentralized optimization in the MATLAB software. The numerical results demonstrate that not only the capacity of distributed generations is fully utilized, but also total costs of smart homes are reduced by more than 27%, and the total grid’s delivery power to smart homes is decreased by more than 24% compared to the inflexible scheduling.

Horizon 2020 Project Analysis by Using Topic Modelling Techniques in the Field of Transport

Abstract Understanding the main research directions in transport is crucial to provide useful and relevant insights. The analysis of Horizon 2020, the largest research and innovation framework, has been already realized in a few publications but rarely for the field of transport. Thus, this article is devoted to fill this gap by introducing a novel application of topic modelling techniques, specifically the Latent Dirichlet Allocation (LDA), in the Horizon 2020 framework for transport projects. The method is using the Mallet software with pre-examined code optimizations. As the first step, a corpus is created by collecting 310 project abstracts; afterward, the texts of abstracts are prepared for the LDA analysis by introducing stop words, optimization criteria, the number of words per topics, and the number of topics. The study successfully uncovers the following five main underlying topics: road and traffic safety, aviation and aircraft, mobility and urban transport, maritime industry and shipping, open and real-time data in transport. Besides that, the main trends in transport are identified based on the frequency of words and their occurrence in the corpus. The applied approach maximizes the added value of the Horizon 2020 initiatives by revealing insights that may be overlooked using traditional analysis methods.

Mechanistic Analysis and Process Simulation of Ethyl Acetate-Ethanol Separation by Complex Solvent Extractive Distillation.

Developing high-performance solvents for extraction and optimizing process technologies is crucial for efficient extractive distillation (ED) separation of azeotrope mixtures. In this paper, computer-aided screening was used to study the ED of azeotrope mixtures in ethyl acetate and ethanol systems using organic solvent dimethyl sulfoxide (DMSO) and ionic liquid (IL) ([EMIM][Ac]). The structural relationship between the ILs and the azeotrope mixture was analyzed by σ-profile, molecular surface electrostatic potential, interaction energy, and separation gradient. Subsequently, process simulation was carried out using Aspen Plus software and global optimization was performed with genetic algorithm, which found that both traditional organic solvents and ILs have good separation effects. But considering the high volatility of organic solvents and low saturation vapor pressure of ILs, it is considered to combine them to further explore the cost and carbon emission advantages in extractive distillation separation. Compared with pure organic solvent and pure ILs separation processes, the TAC of the process using an IL-based mixed solvent process decreased by 5.11 and 21.98%, respectively. The carbon emissions of the mixed extractant process were slightly higher than those of the pure organic solvent process, but the addition of ILs made very little volatilization of organic solvents, saving a charge for extractant use. By improving the process, waste heat is effectively recovered, which can save most of the utility engineering costs, and compared with the previous process, the total alkali consumption and carbon dioxide emissions are reduced by 9.43 and 27.17%, respectively. This exploration provides a theoretical reference for the development application and industrial research of ED processes using IL-based mixed solvents.

A techno-economic assessment of the viability of a photovoltaic-wind-battery storage-hydrogen energy system for electrifying primary healthcare centre in Sub-Saharan Africa

Healthcare facilities in isolated rural areas of sub-Saharan Africa face challenges in providing essential health services due to unreliable energy access. This study examines the use of hybrid renewable energy systems consisting of solar PV, wind turbines, batteries, and hydrogen storage for the electrification of rural healthcare facilities in Nigeria and South Africa. The study deployed the efficacy of Hybrid Optimization of Multiple Energy Resources software for techno-economic analysis and the Evaluation based on the Distance from Average Solution method for multi-criteria decision-making for sizing, optimizing, and selecting the optimal energy system. Results show that the optimal configurations achieve cost-effective levelized energy costs ranging from $0.336 to $0.410/kWh for both countries. For the Nigeria case study, the optimal energy system includes 5 kW PV, 10 kW fuel cell, 10 kW inverter, 10 kW electrolyzer, and 16 kg hydrogen tank. South Africa’s optimal configuration has 5 kW PV, 10 kW battery, 10 kW inverter, and 7.5 kW rectifier. Solar PV provides more than 90 % of energy, with dual axis tracking yielding the highest output: 8,889kWh/yr for Nigeria and 10,470kWh/yr for South Africa. The multi-criteria decision-making analysis reveals that Nigeria’s preferred option is the hybrid system without tracking. In contrast, the horizontal axis, weekly adjustment tracking configuration is optimal for South Africa, considering technical, economic, and environmental criteria. The findings highlight the importance of context-specific optimization for hybrid renewable energy systems in rural healthcare facilities to accelerate Sustainable Development Goals 3 and 7.

Collective hydrogen stand-alone renewable energy systems for buildings in Spain. Towards the self-sufficiency

The article examines the feasibility of implementing standalone hydrogen-based renewable energy systems in Spanish residential buildings, specifically analyzing the optimization of a solar-battery and solar-hydrogen system for a building with 20 dwellings in Spain. The study initially assesses two standalone setups: solar-battery and solar-hydrogen. Subsequently, it explores scenarios where these systems are connected to the grid to only generate and sell surplus energy. A scenario involving grid connection for self-consumption without storage serves as a benchmark for comparison. All system optimizations are designed to meet energy demands without interruptions while minimizing costs, as determined by a techno-economic analysis. The systems are sized using custom software that incorporates an energy management system and employs the Jaya algorithm for optimization. The findings indicate that selling surplus energy can be economically competitive and enhance the efficiency of grid-connected self-consumption systems, representing the study's main innovation. The conclusion highlights the economic and technical potential of an autonomous hybrid energy system that includes hydrogen, with the significant remaining challenge being the development of a regulatory framework to support its technical feasibility in Spain.

Why do we think? The dynamics of spontaneous thought reveal its functions

Abstract Spontaneous thought—mind wandering, daydreaming, and creative ideation—makes up most of everyday cognition. Is this idle thought, or does it serve an adaptive function? We test two hypotheses about the functions of spontaneous thought: First, spontaneous thought improves memory efficiency. Under this hypothesis, spontaneous thought should prioritize detailed, vivid episodic simulations. Second, spontaneous thought helps us achieve our goals. Under this hypothesis, spontaneous thought should prioritize content relevant to ongoing goal pursuits, or current concerns. We use natural language processing and machine learning to quantify the dynamics of thought in a large sample (N = 3,359) of think aloud data. Results suggest that spontaneous thought both supports memory optimization and keeps us focused on current concerns.

АНАЛІЗ МЕТОДІВ ОПТИМІЗАЦІЇ ГЛИБИНИ КВАНТОВОЇ СХЕМИ

This work reviews methods for optimizing quantum circuit depth, encompassing theoretical techniques, algorithmic innovations, and practical applications. Recent advancements demonstrate substantial improvements in circuit depth optimization, yet the issue of scalability remains unresolved. Key research contributions, such as the advanced exact synthesis of Clifford+T circuits, have shown promising results in reducing the number of T-gates, thus enhancing computational efficiency. However, the practical implementation of these methods in real-world noisy environments has yet to be fully evaluated. Further studies have developed automated optimization techniques for large quantum circuits with continuous parameters, significantly minimizing gate counts while maintaining structural integrity. Despite these advancements, challenges persist in assessing the performance of these optimized circuits on actual quantum hardware and integrating them into various quantum algorithms. The qubit routing problem also presents significant hurdles, particularly in systems with limited communication, necessitating effective methodologies to optimize qubit placement and movement. A comprehensive full-stack approach, incorporating hardware, software, and compiler optimizations, has provided valuable insights into architectural comparisons and design strategies. This approach highlights the importance of noise-aware compilation and the need for microarchitecture-specific optimizations to improve program success rates on current quantum devices. This paper underscores the need for scalable, hardware-agnostic optimization methods and extensive experimental validation. Integrating graph neural networks for quantum circuit optimization presents a promising future research direction. The analysis concludes that while significant progress has been made, unresolved issues such as computational complexity, limited experimental validation, and the diversity of quantum algorithms necessitate ongoing research to fully realize the potential of quantum computing. In conclusion, optimizing the depth of quantum circuits is a multifaceted problem requiring continuous research and development. The integration of theoretical advancements with practical implementations is crucial for overcoming current limitations and unlocking the full potential of quantum computing. Continued efforts in this area will be vital for achieving scalable and efficient quantum computations

The NOMAD mini-apps: A suite of kernels from ab initio electronic structure codes enabling co-design in high-performance computing.

This article introduces a suite of mini-applications (mini-apps) designed to optimise computational kernels in ab initio electronic structure codes. The suite is developed from flagship applications participating in the NOMAD Center of Excellence, such as the ELPA eigensolver library and the GW implementations of the exciting, Abinit, and FHI-aims codes. The mini-apps were identified by targeting functions that significantly contribute to the total execution time in the parent applications. This strategic selection allows for concentrated optimisation efforts. The suite is designed for easy deployment on various High-Performance Computing (HPC) systems, supported by an integrated CMake build system for straightforward compilation and execution. The aim is to harness the capabilities of emerging (post)exascale systems, which necessitate concurrent hardware and software development - a concept known as co-design. The mini-app suite serves as a tool for profiling and benchmarking, providing insights that can guide both software optimisation and hardware design. Ultimately, these developments will enable more accurate and efficient simulations of novel materials, leveraging the full potential of exascale computing in material science research.

Spreading code optimization for low-earth orbit satellites via mixed-integer convex programming

Optimizing the correlation properties of spreading codes is critical for minimizing inter-channel interference in satellite navigation systems. By improving the codes’ correlation sidelobes, we can enhance navigation performance while minimizing the required spreading code lengths. In the case of low-earth orbit (LEO) satellite navigation, shorter code lengths (on the order of a hundred) are preferred due to their ability to achieve fast signal acquisition. Additionally, the relatively high signal-to-noise ratio in LEO systems reduces the need for longer spreading codes to mitigate inter-channel interference. In this work, we propose a two-stage block coordinate descent (BCD) method which optimizes the codes’ correlation properties while enforcing the autocorrelation sidelobe zero property. In each iteration of the BCD method, we solve a mixed-integer convex program over a block of 25 binary variables. Our method is applicable to spreading code families of arbitrary sizes and lengths, and we demonstrate its effectiveness for a problem with 66 length-127 codes and a problem with 130 length-257 codes.

A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

In the quest for sustainable urban development, precise quantification of urban green space is paramount. This research delineates the implementation of a Cosine Adaptive Particle Swarm Optimization Long Short-Term Memory (CAPSO-LSTM) model, utilizing a comprehensive dataset from Beijing (1998–2021) to train and test the model. The CAPSO-LSTM model, which integrates a cosine adaptive mechanism into particle swarm optimization, advances the optimization of long short-term memory (LSTM) network hyperparameters. Comparative analyses are conducted against conventional LSTM and Partical Swarm Optimization (PSO)-LSTM frameworks, employing mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) as evaluative benchmarks. The findings indicate that the CAPSO-LSTM model exhibits a substantial improvement in prediction accuracy over the LSTM model, manifesting as a 66.33% decrease in MAE, a 73.78% decrease in RMSE, and a 57.14% decrease in MAPE. Similarly, when compared to the PSO-LSTM model, the CAPSO-LSTM model demonstrates a 58.36% decrease in MAE, a 65.39% decrease in RMSE, and a 50% decrease in MAPE. These results underscore the efficacy of the CAPSO-LSTM model in enhancing urban green space area prediction, suggesting its significant potential for aiding urban planning and environmental policy formulation.

Evaluation of feeding table optimizations in Pacific white shrimp nursery biofloc systems

A 45-day experiment was conducted to optimize feeding rates in the nursery phase of Pacific white shrimp reared in biofloc systems (BFT). Four treatments were evaluated in quadruplicate, according to the Van Wyk table: maximum feeding rate; minimum feeding rate; minimum feeding rate minus 10%; and maximum feeding rate plus 10%. Post-larvae (0.08 ± 0.00 g) were cultured at the density of 2,000 shrimp·m-3, and water quality, solids production, and productive performance were all monitored. No significant difference was observed in final mean weight (1.47 ± 0.17 g), productivity (2.34 ± 0.20 kg·m-3) and survival (85.29 ± 5.44%) among treatments, but feed conversion ratio was significantly lower in the minimum feeding rate and minimum feeding rate minus 10% treatments, indicating efficient feed conversion without compromising growth. These treatments also resulted in lower values of toxic nitrogen compounds and total suspended solids, suggesting a positive impact on water quality. Although these rates proved suitable, continuous adjustments are needed owing to variations in the BFT system. This study provides guidelines for optimizing feeding management in superintensive BFT nursery systems.

Long Short-Term Memory Optimization Using Hybrid Sparrow Search Algorithm and Particle Swarm Optimization in Prediction of Water Level at Sluice Gates

Long Short-Term Memory Optimization Using Hybrid Sparrow Search Algorithm and Particle Swarm Optimization in Prediction of Water Level at Sluice Gates

Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6

Abstract. Lagrangian particle dispersion models are indispensable tools for the study of atmospheric transport processes. However, Lagrangian transport simulations can become numerically expensive when large numbers of air parcels are involved. To accelerate these simulations, we made considerable efforts to port the Massive-Parallel Trajectory Calculations (MPTRAC) model to graphics processing units (GPUs). Here we discuss performance optimizations of the major bottleneck of the GPU code of MPTRAC, the advection kernel. Timeline, roofline, and memory analyses of the baseline GPU code revealed that the application is memory-bound, and performance suffers from near-random memory access patterns. By changing the data structure of the horizontal wind and vertical velocity fields of the global meteorological data driving the simulations from structure of arrays (SoAs) to array of structures (AoSs) and by introducing a sorting method for better memory alignment of the particle data, performance was greatly improved. We evaluated the performance on NVIDIA A100 GPUs of the Jülich Wizard for European Leadership Science (JUWELS) Booster module at the Jülich Supercomputing Center, Germany. For our largest test case, transport simulations with 108 particles driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, we found that the runtime for the full set of physics computations was reduced by 75 %, including a reduction of 85 % for the advection kernel. In addition to demonstrating the benefits of code optimization for GPUs, we show that the runtime of central processing unit (CPU-)only simulations is also improved. For our largest test case, we found a runtime reduction of 34 % for the physics computations, including a reduction of 65 % for the advection kernel. The code optimizations discussed here bring the MPTRAC model closer to applications on upcoming exascale high-performance computing systems and will also be of interest for optimizing the performance of other models using particle methods.

Generation of genetic codes with 2-adic codon algebra and adaptive dynamics

This is a brief review on modeling genetic codes with the aid of 2-adic dynamical systems. In this model amino acids are encoded by the attractors of such dynamical systems. Each genetic code is coupled to the special class of 2-adic dynamics. We consider the discrete dynamical systems, These are the iterations of a function F:Z2→Z2, where Z2 is the ring of 2-adic numbers (2-adic tree). A genetic code is characterized by the set of attractors of a function belonging to the code generating functional class. The main mathematical problem is to reduce degeneration of dynamic representation and select the optimal generating function. Here optimality can be treated in many ways. One possibility is to consider the Lipschitz functions playing the crucial role in general theory of iterations. Then we minimize the Lip-constant. The main issue is to find the proper biological interpretation of code-functions. One can speculate that the evolution of the genetic codes can be described in information space of the nucleotide-strings endowed with ultrametric (treelike) geometry. A code-function is a fitness function; the solutions of the genetic code optimization problem are attractors of the code-function. We illustrate this approach by generation of the standard nuclear and (vertebrate) mitochondrial genetics codes.

Event driven high speed data acquisition with IEEE 1588 synchronization for long pulse operations of Indian Test Facility for ITER DNB

The Indian Test Facility (INTF) is a negative Hydrogen ion based 100 kV, 60A, 5 Hz modulated NBI system having duty cycle of 3 s ON time to 20 s OFF time. Prime objective of the facility is to characterize ITER Diagnostic Neutral Beam (DNB) with full specifications, prior to shipment and installation in ITER. In order to fully characterize ITER DNB, a matured Data Acquisition and Control system (DACS) is needed which will be responsible for supervisory control of the facility and for acquiring and monitoring signals at run time. Apart from conventional DACS, high speed data acquisition is needed for diagnostics and other signals with sampling rate requirements of 100 MS/s. For acquiring data from such systems it is imperative that the data be acquired only for certain duration on the occurrence of the event. Such acquisition systems are classified as event driven data acquisition system and are widely used in fusion engineering community.Development and implementation of high speed event driven data acquisition systems pose challenges in terms of data buffering, data streaming and synchronization. The precise timing of the event occurrence with respect to the main pulse should also be known for which a timing and synchronization system is needed. To fulfill this purpose, IEEE 1588 protocol offers a network based method of synchronizing multiple nodes based on Ethernet with sub microsecond accuracy. The present work describes the development of an event driven high speed data acquisition in synchronization with IEEE 1588 based timing network. The work proposes the time stamping of the event from synchronized network integrated with the event acquisition platform. The developed system has been tested for pulse durations longer than 3600 s. The performance of event data acquisition system has been thoroughly characterized and improved by applying various software optimizations in LabVIEW. The present work although intended to be used for INTF can be used for any such facility in fusion or other areas.The paper provides results of IEEE 1588 based timing network in various configurations and event data acquisition system developed on high speed FPGA controlled digitizer board with prototype results.

Coding Optimization for an Ecological non-linear Age-structured Fish Population Model with a Shepherd Recruitment Function

The study of the optimally density-regulated recruitment plays a crucial role in the overall management and determining of fishing efforts of marine fish populations. This paper will develop a Matlab code can determine what an optimal recruitment density regulation strategy would be for the un-fished population and what it changes to when fishing efforts are applied. This code can determine threshold values used to avoid fisheries from collapsing. The code will be applied to the Gulf of California Pacific sardine (Sardinops caeruleus) population. An age structured design with a Shepherd density regulating stock-recruitment function is used in this study. In this work, the parameter which is varied is captured in the Shepherd recruitment function and is associated with behavioral interventions such as selecting different drift routes and clumping on resources that will increase egg-larvae survival to the recruitment stage. A detailed background study of other models is explored, then a derivation of the Shepherd recruitment model is developed from the traditional discrete model. A special MATLAB code is then developed to illustrate the pair-wise invasive plots for the model structures. The code is for theoretical analysis of fish population models.

Boneh-Shaw fingerprint code and Tardos code analysis and optimization in minority collusion attack

Fingerprint codes are required to protect digital rights. These codes were embedded in digital media before distribution. Each of these digital media codes is unique. These codes identify pirated digital media copies. This method is used to identify the creator of an illegal copy. But such principles tend to be collectively attacked. In the Collusion Attack, criminal users conspired to integrate the attacks into these codes.Criminal users devise and switch digital fingerprint media with different codes to print the same content. The code is modified to accuse the innocent user. Thus, this attack is a threat to the protection of digital rights.The minority collusion assault being conducted against the Boneh-Shaw and Tardos codes to assess how serious this threat is can be used to demonstrate the significance of the research. Understanding these ideas is essential for improving the protection of digital rights in the face of changing security issues in the digital sphere. This study launches a minority collusion attack against both the Tardos code and the Boneh-Shaw fingerprint code to analyze the impact. The development of more resilient, adaptable codes to prevent changing collusion strategy is part of the prospective focus for Boneh-Shaw Fingerprint Code and Tardos Code Analysis and Optimization in Minority Collusion Attacks.

Pattern matching algorithms in blockchain for network fees reduction

Blockchain received a vast amount of attention in recent years and is still growing. The second generation of blockchain, such as Ethereum, allows execution of almost any program in EVM, making it a global protocol for distributed applications. The code deployment and each operation performed in EVM cost the network fee called gas, whose price varies and can be significant. That is why code optimization and well-chosen algorithms are crucial in programming on the blockchain. This paper evaluates the gas usage of several exact pattern matching algorithms on the EVM. We also propose an efficient implementation of the algorithms in the Solidity/YUL language. We evaluate the gas fees of all the algorithms for different parameters (such as pattern length, alphabet size, and text size). We show a significant gas fee and execution time reduction with up to 22-fold lower gas usage and 55-fold speed-up compared to StringUtils (a popular Solidity string library).

Small Solid-Model Rocket Design and Soft Landing Trajectory Planning

A recoverable rocket is one of the keys to reusable launch technology. Currently, most recoverable rockets are powered by liquid engines, which are costly and complex compared to solid engines. In this paper, we perform soft landing trajectory planning for the designed solid rocket. First, a small model rocket equipped with a landing engine is innovatively designed. Second, a six-degree-of-freedom motion model of the rocket is established, and a minimum-landing-error problem is formed based on the landing engine. Third, successive convexification algorithm is applied to solve it. In the algorithm, a method of quaternion constraint correction is proposed to avoid the infeasibility of the problem. Lastly, the correctness and robustness of the algorithm are verified via three simulations. The possibility of rapid trajectory generation is verified by code optimization on a high-performance digital signal processor.

Are Learnable Prompts the Right Way of Prompting? Adapting Vision-and-Language Models with Memory Optimization

Are Learnable Prompts the Right Way of Prompting? Adapting Vision-and-Language Models with Memory Optimization