Optimization Perspective Research Articles

Development and Application of Green Catalysts: Challenges, Optimization, and Future Perspectives

This paper comprehensively reviews the development and application of green catalysts in modern chemical industries, with a focus on analyzing the significant advantages of various types of green catalysts, such as metal-organic frameworks (MOFs) and enzyme catalysts, in improving reaction efficiency, reducing energy consumption, and minimizing by-product generation. The paper delves into the design and synthesis of green catalysts, optimization of reaction conditions, and technologies for catalyst recycling and regeneration, highlighting their key roles in enhancing catalyst performance. Despite challenges such as high material costs, complex synthesis processes, and issues with stability and durability in practical applications, continuous technological innovation and process improvements offer promising solutions. As global attention to sustainable development grows, green catalysts are expected to have broad applications in emerging fields such as carbon capture and utilization, renewable energy conversion, and environmental remediation, thereby driving the green transformation and low-carbon development of the chemical industry and providing strong technical support for achieving global sustainability goals.

Connective Urban Greenway Route Planning: A Spatial Optimization Perspective

Urban greenways, as vital green infrastructures, offer a range of societal, environmental, and ecological benefits to city dwellers. However, planning their routes remains a complex task for urban planners. While most studies emphasize either maximizing the number of residents served or enhancing the environmental benefits along these greenways, the role of urban greenways in linking existing facilities like tourist attractions, urban parks, public transport stations, and other greenways has received less attention. This paper introduces an innovative spatial optimization model for the planning of connective urban greenway routes. The model aims to link these facilities while also maximizing the population’s access to the greenway. We developed a set of models applicable to different objectives of connecting existing facilities, such as maximizing the number of facilities to be covered, covering all facilities, and minimizing the distance between facilities and planned greenways. Bi-objective scenarios, including weighted and lexicographical models, are also presented. We delineated the MILP formulation of the proposed models. The paper includes simulation tests with empirical data from Lhasa, China, validating the model’s practicality and computational efficiency.

Dynamic behavior and transfer characteristics of CO2-enriched bubbles within Spirulina sp. suspension under various aeration conditions using the high-speed imaging technique

The biological CO2 fixation method through microalgae photosynthesis has received considerable attention to alleviate the trend of global warming. CO2-enriched gas is generally aerated into the microalgae suspension in the form of bubbles through the gas distributors. Dynamic behavior and transfer characteristics of CO2-enriched bubbles are crucial to microalgae cells growth and CO2 bio-fixation. A visual experimental system based on the high-speed camera was constructed in this work to obtain the dynamic behavior and transfer characteristics of CO2-enriched bubbles within Spirulina sp. suspension. CO2-enriched bubbles movement and dissolution characteristics were comprehensively investigated under various CO2 concentrations, gas distributor aperture size, aeration rates, and Spirulina sp. biomass densities. Experimental results indicate that the optimal CO2 dissolution mass transfer and absorption rate were attained under the CO2 concentration of 5 %, gas distributor aperture diameter of 10 μm, and aeration rate of 0.1–0.3 vvm. Moreover, as Spirulina sp. biomass density increased, the bubble average diameter decreased, and rising velocity slowed while the volumetric mass transfer coefficient and CO2 absorption rate elevated. To summarize, this work may guide future efforts to enhance the photobioreactors (PBRs) performance from the perspective of aeration conditions optimization.

Design of agricultural question answering information extraction method based on improved BILSTM algorithm

With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the BE-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python’s Scrapy crawler framework to obtain the information of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values. Secondly, the information extraction converts the semi-structured data by using entity extraction methods. Thirdly, the BERT (Bidirectional Encoder Representations from Transformers) algorithm is introduced to improve the performance of the BILSTM algorithm. After comparing with the BERT-CRF (Conditional Random Field) and BILSTM algorithm, the result shows that the BE-BILSTM algorithm has better information extraction performance than the other two algorithms. This study improves the accuracy of the agricultural information recommendation system from the perspective of information extraction. Compared with other work that is done from the perspective of recommendation algorithm optimization, it is more innovative; it helps to understand the semantics and contextual relationships in agricultural question and answer, which improves the accuracy of agricultural information recommendation systems. By gaining a deeper understanding of farmers’ needs and interests, the system can better recommend relevant and practical information.

If cheating is optimisation then assessment must not be pure

The emergence of generative AI has broadened the question of how to ensure academic integrity. Where, in the past, many tools sought to counter specific threats to integrity (for example, detecting plagiarism or outsourcing), we now need to consider integrity more fundamentally: What work can we be assured the student did? One way of viewing cheating is from the perspective of optimisation: producing a result without performing the work. This perspective creates an interesting parallel with computer programming. A programmer often wants their compiler to optimise their program, altering its instructions to make it run more efficiently, without affecting its observable behaviour. Some characteristics of programs make this easier or harder to do. For example, a function is said to be “pure” if it always produces the same outputs for the same inputs and has no other effects. If the result is known, then performing the function can be invisibly replaced by inserting its result. If, however, a function has an observable effect, then the work of performing the function cannot be optimised away. This paper explores academic integrity assurance through the lens of “effect types”. In functional languages, functions are often written in such a manner that the types of effects they have are explicit and tracked by the compiler. The paper likens this to tracking the types of academic integrity measures that are present in each assessment, how they compose across a course, and therefore what observable effects are present to assure each learning outcome.

Design Issues and Solutions for Improving the Comfort of Neo-Ming Style Chair

This paper explores the design issues and solutions for improving the comfort of new Ming-style chairs. It first analyzes the historical origins, traditional aesthetics, and modern users' new demands for comfort in these chairs. Then, it highlights the comfort-related challenges in terms of chair form, materials, structure, and usage scenarios. The study proposes design principles from the perspectives of ergonomics, material innovation, structural optimization, and modern usage scenarios. Specific strategies to enhance comfort are provided, such as optimizing the angle between the chair back and chair surface, improving cushion materials, optimizing the chair's supporting structure, and incorporating intelligent design. This paper also discusses the challenges of integrating technology with traditional design, the application of eco-friendly materials, and the diverse demands of consumer groups. Finally, it summarizes the future trends and challenges in the comfort-focused design of Neo-Ming style chair.

Preparing for Patient Care: A Pre-clerkship Curricular Thread to Improve Learners’ Knowledge, Attitudes, and Confidence Treating Patients at Risk for Suicide

AbstractSuicide is a leading cause of death. Unfortunately, stigma still surrounds depression and mental health treatment. Many health care providers are uncomfortable broaching the topic with patients. There is an urgent need to better equip future physicians to compassionately identify and treat patients at risk for suicide. To address this problem, we created a novel suicide prevention spiral curricular thread for advanced pre-clerkship medical students. Mixed methods surveys were administered before and after the curriculum. Following completion of the curriculum, learners’ confidence identifying and treating patients at risk of suicide significantly increased. Attitudes including professional confidence, therapeutic optimism, and generalist perspective measured by the Revised Depression Attitudes Questionnaire also increased, indicating reduced stigmatizing attitudes and increased knowledge and confidence. Participants reported that engaging in the standardized patient encounter was the most impactful event, although the prior events provided necessary foundational training. This study demonstrates the feasibility and effectiveness of a curricular thread approach during the pre-clerkship phase for training medical students in suicide prevention to prepare them for patient care. Other medical schools can use this framework to design and integrate suicide prevention training into their own curricula.

Integrating digitalization, environmental innovations, and green energy supply to ensure green production in China’s textile and fashion industry: environmental policy and laws optimization perspective

Integrating digitalization, environmental innovations, and green energy supply to ensure green production in China’s textile and fashion industry: environmental policy and laws optimization perspective

Research on Enterprise Financial Information Risk Identification and Dynamic Control Management Model Based on Genetic Algorithm

In the contemporary global economic landscape, the imperative for enterprise financial informatization as a catalyst for efficiency, cost reduction, and innovation is evident. However, the deepening integration of informatization introduces corresponding risks, necessitating the establishment of effective risk management systems. This paper proposes a genetic algorithm-based model for enterprise financial informatization risk management, aiming to offer precise and actionable solutions. The study integrates genetic algorithms to enhance the adaptability and flexibility of the risk management model in the dynamic information environment. Through in-depth research on corporate financial informatization risks, a multi-dimensional risk management control model is constructed, considering technical, organizational, and environmental factors. The genetic algorithm introduces a new perspective for model optimization, enabling efficient search and optimization capabilities. The model not only identifies but also controls and governs risks, providing timely intervention and effective risk management. Moreover, the genetic algorithm facilitates intelligent decision support for risk management by adapting to changing environments. Empirical analysis validates the model’s feasibility and effectiveness in real enterprise cases, emphasizing its practicality.

Research on the optimization of investment structure of China's high-tech industries under the new development model of dual circulation

On May 14, 2021, at the Standing Committee of the Political Bureau of the CPC Central Committee, General Secretary Xi Jinping first proposed the concept of "dual circulation", emphasizing the construction of a new development pattern in which domestic and international dual circulations promote each other. Capital circulation is the primary link in opening up the circulation, and high-tech industries are the key areas in this new development pattern. In this context, it is of great significance and value to study the optimization of the investment structure of high-tech industries. This study takes the new dual circulation model as the starting point and conducts an in-depth analysis of high-tech industries from the perspective of investment structure optimization. By applying theories such as capital circulation, dynamic comparative advantage, and investment portfolio, the relationship between China's current economic development status and dual circulation is explained. At the same time, the development status of China's high-tech industries is compared with that of foreign high-tech industries, and the advantages, gaps, and problems between China's high-tech industries and developed countries are analyzed. The research plan explores the rationality of the investment structure of high-tech industries in the internal circulation, external circulation, and dual circulation from multiple perspectives such as industrial growth, investment efficiency, industrial upgrading, and global value chain. This will provide a theoretical basis and practical guidance for optimizing the investment structure of high-tech industries and help China achieve high-quality development under the new development pattern.

Sequential convex programming without penalty function for reentry trajectory optimization problem

Sequential convex programming (SCP) has been extensively utilized in reentry trajectory optimization due to its high computational efficiency. However, the current SCP approaches primarily rely on penalty function, where the selection of the penalty function weight presents a significant challenge. In this paper, an improved trust region shrinking SCP algorithm is proposed that separates the treatment of the objective function and constraint violation without the need for selecting penalty function weight and introduction of slack variables. Firstly, from the perspective of multi-objective optimization, the filter and acceptance condition are introduced to ensure that the proposed algorithm converges to feasible solutions and then to the optimal solution based on switching condition and sufficient condition. Then an effective feasibility restoration phase is proposed to address infeasibility of subproblems without introducing slack variables, while ensuring the robustness of the proposed algorithm. Additionally, a theoretical analysis is provided to guarantee the convergence of the algorithm. Finally, simulations are conducted to verify that the proposed algorithm demonstrates a 69.54% improvement in average solution time and stronger robustness compared to basic trust region shrinking SCP algorithm. Simultaneously, the proposed algorithm also demonstrates an advantage in solving speed compared to a particular advanced penalty function-based SCP algorithm.

An efficient three-dimensional node localization using recurrent neural networks in unmanned aerial vehicle-assisted wireless networks: an optimization perspective

PurposeUnlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.Design/methodology/approachThe paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.FindingsSimulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.Originality/valueConventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

Addressing the multiplicity of optimal solutions to the Clonal Deconvolution and Evolution Problem

The Clonal Deconvolution and Evolution Problem consists on unraveling the clonal structure and phylogeny of a tumor using estimated mutation frequency values obtained from multiple biopsies containing mixtures of tumor clones. In this article, we tackle the problem from an optimization perspective and we explore the number of optimal solutions for a given instance. Even in ideal scenarios without noise, we demonstrate that the Clonal Deconvolution and Evolution Problem is highly under-determined, leading to multiple solutions. Through a comprehensive analysis, we examine the factors contributing to the multiplicity of solutions. We find that as the number of samples increases, the number of optimal solutions decreases. Additionally, we explore how this phenomenon operates across various tumor topology scenarios. To address the issue of the existence of multiple solutions, we present sufficient conditions under which the problem can have a unique solution, and we propose a linear programming-based algorithm that leverages mutation orderings to generate instances with a single solution for a given topology. This algorithm encounters numerical challenges when applied to large instance sizes so, to overcome this, we propose a heuristic adaptation that enables the algorithm’s use for instances of any size.

Interference Mitigation for Network-Level ISAC: An Optimization Perspective

Interference Mitigation for Network-Level ISAC: An Optimization Perspective

Exploring emission spatiotemporal pattern and potential reduction capacity in China's aviation sector: Flight trajectory optimization perspective

China's rapid expansion of civil aviation has led to an increase in pollution-related issues, causing adverse health effects on populations near airports and downwind. Accurately quantifying aviation emissions is essential for effective emission management. Here, we developed a high-resolution aviation emissions inventory for China by employing a bottom-up approach that relied on daily flight schedules. By using the Aeronautical Information Publication (AIP) to reproduce real-world flight routes rather than conventional great-circle routes, we improved the accuracy of emissions and investigated the potential for reducing these emissions. Our findings demonstrated substantial variations in domestic civil aviation emissions both spatially and temporally. Emissions peaked in most provinces during Chinese holidays, particularly the Chinese Lunar New Year and summer holidays, highlighting the importance of detailed activity data for accurate emissions calculations. Therefore, we recommend extensive utilization of real-world flight routes, particularly in areas with limited Automatic Dependent Surveillance-Broadcast (ADS-B) coverage since they provide more accurate representations of actual flight trajectories. Our study also identified regions like Shaanxi, Sichuan, Beijing, and their surroundings having considerable potential for emission reduction due to substantial deviations from great-circle routes. This approach can enhance the accuracy and spatiotemporal resolution of aviation emissions at national and global scales throughout the year, without relying on extensive, long-term real-time flight trajectories. Additionally, it provides a unique way to quantify the potential for emission reductions across provinces in civil aviation, ultimately contributing to mitigating pollution-related health impacts from aviation emissions and promoting a more sustainable aviation industry.

A survey on optimization studies of group centrality metrics

AbstractCentrality metrics have become a popular concept in network science and optimization. Over the years, centrality has been used to assign importance and identify influential elements in various settings, including transportation, infrastructure, biological, and social networks, among others. That said, most of the literature has focused on nodal versions of centrality. Recently, group counterparts of centrality have started attracting scientific and practitioner interest. The identification of sets of nodes that are influential within a network is becoming increasingly more important. This is even more pronounced when these sets of nodes are required to induce a certain motif or structure. In this study, we review group centrality metrics from an operations research and optimization perspective for the first time. This is particularly interesting due to the rapid evolution and development of this area in the operations research community over the last decade. We first present a historical overview of how we have reached this point in the study of group centrality. We then discuss the different structures and motifs that appear prominently in the literature, alongside the techniques and methodologies that are popular. We finally present possible avenues and directions for future work, mainly in three areas: (i) probabilistic metrics to account for randomness along with stochastic optimization techniques; (ii) structures and relaxations that have not been yet studied; and (iii) new emerging applications that can take advantage of group centrality. Our survey offers a concise review of group centrality and its intersection with network analysis and optimization.

A key distribution technique for wireless sensor networks using spanning trees

Protocols for encrypting communications in wireless sensor networks have rarely been addressed from a classical network optimization perspective. An underlying procedure of such protocols, known as key distribution, involves assigning cryptographic keys to sensors, which are then used to encrypt and decrypt transmitted data. In context of maximizing communications security objectives, the combinatorial nature of assigning keys to sensors with limited memory capacities exhibits properties conducive to developing methods for finding optimal key distributions. This work focuses on the q-Composite key distribution/assignment protocol, which requires that two sensors share at least q common keys to securely exchange data. As a means of reducing the likelihood of large-scale information breaches if a small number of keys are compromised, a security objective of maximizing the number of non-redundant (unique) keys assigned to the sensors is considered. It is demonstrated that node (sensor) degrees in a spanning tree, along with their memory capacities, can be used to exactly determine the maximum possible number of non-redundant keys assigned. Leveraging on these properties, our method reduces the problem of assigning a maximum number of keys to that of finding a degree-bounded spanning tree in a network. A polynomial time solution algorithm along with a linear time algorithm for assigning a maximum number of unique keys is introduced. Broadly, the proposed method provides a novel approach for designing, analyzing, or identifying secure key distributions in large-scale networks by using properties of the underlying graphs.

Optimal truss design with MOHO: A multi-objective optimization perspective.

This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The Hippopotamus Optimizer (HO) is a novel approach in meta-heuristic methodology that draws inspiration from the natural behaviour of hippos. The HO is built upon a trinary-phase model that incorporates mathematical representations of crucial aspects of Hippo's behaviour, including their movements in aquatic environments, defense mechanisms against predators, and avoidance strategies. This conceptual framework forms the basis for developing the multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions and size constraints concerning stresses on individual sections and constituent parts, these problems also involved competing objectives, such as reducing the weight of the structure and the maximum nodal displacement. The findings of six popular optimization methods were used to compare the results. Four industry-standard performance measures were used for this comparison and qualitative examination of the finest Pareto-front plots generated by each algorithm. The average values obtained by the Friedman rank test and comparison analysis unequivocally showed that MOHO outperformed other methods in resolving significant structure optimization problems quickly. In addition to finding and preserving more Pareto-optimal sets, the recommended algorithm produced excellent convergence and variance in the objective and decision fields. MOHO demonstrated its potential for navigating competing objectives through diversity analysis. Additionally, the swarm plots effectively visualize MOHO's solution distribution of MOHO across iterations, highlighting its superior convergence behaviour. Consequently, MOHO exhibits promise as a valuable method for tackling complex multi-objective structure optimization issues.

Deep reinforcement learning as multiobjective optimization benchmarks: Problem formulation and performance assessment

The successful deployment of Deep learning in several challenging tasks has been translated into complex control problems from different domains through Deep Reinforcement Learning (DRL). Although DRL has been extensively formulated and solved as single-objective problems, nearly all real-world RL problems often feature two or more conflicting objectives, where the goal is to obtain a high-quality and diverse set of optimal policies for different objective preferences. Consequently, the development of Multi-Objective Deep Reinforcement Learning (MODRL) algorithms has gained a lot of traction in the literature. Generally, Evolutionary Algorithms (EAs) have been demonstrated to be scalable alternatives to the classical DRL paradigms when formulated as an optimization problem. Hence it is reasonable to employ Multi-objective Evolutionary Algorithms (MOEAs) to handle MODRL tasks. However, there are several factors constraining the progress of research along this line: first, there is a lack of a general problem formulation of MODRL tasks from an optimization perspective; second, there exist several challenges in performing benchmark assessments of MOEAs for MODRL problems. To overcome these limitations: (i) we present a formulation of MODRL tasks as general multi-objective optimization problems and analyze their complex characteristics from an optimization perspective; (ii) we present an end-to-end framework, termed DRLXBench, to generate MODRL benchmark test problems for seamless running of MOEAs (iii) we propose a test suite comprising of 12 MODRL problems with different characteristics such as many-objectives, degenerated Pareto fronts, concave and convex optimization problems, etc. (iv) Finally, we present and discuss baseline results on the proposed test problems using seven representative MOEAs.

Stiffness and stability of bamboo stem- A optimal design perspective

During the evolution process, a bamboo stem achieves a significant height (up to 20 m) to fulfil its phototropic requirements. While on land, the stem is mostly subjected to bending load which makes it liable to fail by uprooting. However, this failure is prohibited by smart structure of bamboo stem which includes graded arrangement of fibre bundles in the cross-section and a tapered cantilever form of the stem. This paper attempts to understand the optimal design of bamboo stem through the relationship between the stellar arrangement of stiff fibre bundles in the cross-section and the tapered form. In this work, a comparison between two types of stellar arrangement, namely uniform and graded, is presented in view of non-linear bending analysis through elastica theory and fracture-induced delamination, both numerically. It is observed from the results that a bamboo stem prefers to evolve with graded stellar arrangement which provides gradation of stiffness and toughness over the cross-section; the trend in toughness being opposite to that of stiffness. Moreover, interplay of stellar arrangement and gradation of stiffness-toughness thereof is found to be the governing mechanism for ensuring its mechanical integrity and stability in view of an optimal design perspective. The smart structure of bamboo is recommended for bio-mimicking.