Complex Applications Research Articles

Multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization for expensive optimization problems

Surrogate-assisted evolutionary algorithms (SAEAs) have been successfully applied to solve computationally expensive optimization problems. However, most SAEAs struggle to achieve good results in solving complex multimodal problems, especially high-dimensional ones. Moreover, for problems with complex landscapes, SAEAs typically require constructing complex global surrogates to model the landscape and performing many iterations to identify the surrogate’s optimum, thereby reducing the efficiency of SAEAs. To deal with these issues, this paper proposes a multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization (MHS-QPSO) algorithm for expensive optimization problems. To better balance exploration and exploitation, a search behavior selection strategy is proposed, enabling MHS-QPSO to appropriately switch between global and local searches. For the global search, the search space is divided into multiple regions that can adaptively adjust the size of the areas. A surrogate is constructed in each region, requiring only a small number of QPSO iterations to find the optimum of each surrogate. Furthermore, a novel reliability-based criterion is proposed to screen candidate solutions in different regions for exact evaluations, which can save the number of exact function evaluations and can rapidly improve the fitting accuracy of the surrogates in regions with superior fitness. During local searches, a dynamic boundary adjustment strategy is introduced to guide the QPSO to faster approach the potential optimal region. Experimental results on seven benchmark functions with dimensions from 10 to 100, and on a complex real application, demonstrate that MHS-QPSO significantly outperforms several state-of-the-art algorithms within a limited computational budget. Code for MHS-QPSO is available at https://github.com/quanshuzhang/MHS-QPSO.git.

A new insight on the effects of Schiff Base Iron (III) complexes in breast cancer cells for clinical radiotherapy

PurposeBreast cancer is a significant global health concern, and researchers strive to enhance radiotherapy outcomes while minimizing the side effects. Schiff Base Iron (III) Complexes are one of the prospective elements that can be used as radiosensitizer or radioprotective agents in cancer radiotherapy. This study investigates the potential effects of Schiff base (ligand 2; L2) with Fe(III) in MCF-7 breast cancer cells under clinical radiotherapy treatment. MethodsThe effects of the Schiff Base Iron (III) Complexes were measured using clonogenic assay with MCF-7 breast cancer cells. The cells were irradiated with megavoltage 6 MV photon, 6 MeV electron and high dose rate (HDR) brachytherapy with 192Ir source at different doses. Intercellular localization of Fe(III)-L2 complexes and antioxidant activities were also investigated. ResultsThe Fe(III)-L2 complexes were observed to be internalized by cellular nuclei without any effects on the cells. Interestingly, the Fe(III)-L2 complexes indicate radioprotective effects which provide intriguing insight towards application of metal ions complexes as radioprotector in cancer radiotherapy. The Fe(III)-L2 complexes also exhibit scavenging activities of free radical which further proved the antioxidative properties and radioprotective effects. ConclusionThe Fe(III)-L2 complexes show the radioprotective effects and antioxidant properties in MCF-7 cells, particularly for HDR brachytherapy. The findings suggest potential applications of the Fe(III)-L2 complexes as radioprotector agents in clinical radiotherapy.

Precision across temperatures: Eu/Tb luminescent thermometer with exceptionally high and stable sensitivity from 180 to 320 K

Luminescence thermometry, leveraging the temperature-dependent luminescent properties of coordination compounds, offers precise and remote temperature measurement. However, many existing luminescent thermometers exhibit a narrow peak in temperature sensitivity, restricting their effective operating range. In this study, we synthesized new complexes [LnL2(NO3)(TPPO)2], where Ln = Eu (1), Tb (2), Gd (3), Tb0.94Eu0.06 (4), (HL - 4,4,4-trifluoro-1-(furan-2-yl) butan-1,3-dione, TPPO - triphenylphosphine oxide, which demonstrate an exceptionally wide and stable range of maximum temperature sensitivity of 3 %×K−1, spanning from 180 to 320 K. Furthermore, the temperature uncertainty remained low and stable across the same temperature region, ensuring reliable measurements. An unusual behavior was observed in the ion-to-ion energy transfer process rate constant, which exhibited an additional peak between 120 and 180 K. This phenomenon warrants further investigation to fully understand the underlying mechanisms. The obtained results allow for creation of new highly efficient luminescent thermometers tailored for specific complex applications, including cryobiology and microfluidics.

MAPEAMENTO DE SUSCETIBILIDADE À DESASTRES NATURAIS ATRAVÉS DO USO DA LÓGICA NEBULOSA NO MUNICÍPIO DE ALEGRE–ES

Among the various problems that Brazil currently faces, few have a social and human impact as significant as those related to natural phenomena and their negative consequences for society, especially geotechnical events and floods. In a country like Brazil, which suffers from a lack of urban planning and adequate investments in infrastructure to prevent this type of occurrence in various spheres, recurring tragedies accumulate year after year, always accompanied by the perception that they could have been avoided. On the other hand, the geosciences have been increasingly open to new geotechnologies. In this context, the present work presented new paths that offer useful resources to seek short to medium-term solutions, helping to overcome the various challenges related to this theme, both at the national, regional and municipal levels. Thus, the methodology related to geotechnologies and its various techniques was used, with emphasis on Fuzzy Logic, which was the main pillar of this study. In the end, the methodology adopted proved to be effective in pointing out the areas of greatest susceptibility to geotechnical problems and flooding, revealing great potential for future applications of greater complexity and scope.

Joint Approach for Vehicle Routing Problems Based on Genetic Algorithm and Graph Convolutional Network

The logistics demands of industries represented by e-commerce have experienced explosive growth in recent years. Vehicle path-planning plays a crucial role in optimization systems for logistics and distribution. A path-planning scheme suitable for an actual scenario is the key to reducing costs and improving service efficiency in logistics industries. In complex application scenarios, however, it is difficult for conventional heuristic algorithms to ensure the quality of solutions for vehicle routing problems. This study proposes a joint approach based on the genetic algorithm and graph convolutional network for solving the capacitated vehicle routing problem with multiple distribution centers. First, we use the heuristic method to modularize the complex environment and encode each module based on the constraint conditions. Next, the graph convolutional network is adopted for feature embedding for the graph representation of the vehicle routing problem, and multiple decoders are used to increase the diversity of the solution space. Meanwhile, the REINFORCE algorithm with a baseline is employed to train the model, ensuring quick returns of high-quality solutions. Moreover, the fitness function is calculated based on the solution to each module, and the genetic algorithm is employed to seek the optimal solution on a global scale. Finally, the effectiveness of the proposed framework is validated through experiments at different scales and comparisons with other algorithms. The experimental results show that, compared to the single decoder GCN-based solving method, the method proposed in this paper improves the solving success rate to 100% across 15 generated instances. The average path length obtained is only 11% of the optimal solution produced by the GCN-based multi-decoder method.

Advances in and Future Perspectives on High-Power Ceramic Lasers

Advancements in laser glass compositions and manufacturing techniques has allowed the development of a new category of high-energy and high-power laser systems which are being used in various applications, such as for fundamental research, material processing and inertial confinement fusion (ICF) technologies research. A ceramic laser is a remarkable revolution in solid state lasers. It exhibits crystalline properties, high yields, better thermal conductivity, a uniformly broadened emission cross-section, and a higher mechanical constant. Polycrystalline ceramic lasers combine the properties of glasses and crystals, which offer the unique advantages of high thermal stability, excellent optical transparency, and the ability to incorporate active laser ions homogeneously. They are less expensive and have a similar fabrication process to glass lasers. Recent developments in these classes of lasers have led to improvements in their efficiency, beam quality, and wavelength versatility, making them suitable for a broad range of applications, such as scientific research requiring ultra-fast laser pulses, medical procedures like laser surgery and high-precision cutting and welding in industrial manufacturing. The future of ceramic lasers looks promising, with ongoing research focused on enhancing their performance, developing new doping materials and expanding their functional wavelengths. The ongoing progress in high-power ceramic lasers is continuously expanding the limits of laser technology, therefore allowing the development of more powerful and efficient systems for a wide range of advanced and complex applications. In this paper, we review the advances, limitations and future perspectives of ceramic lasers.

Rapid-Learning Collaborative Pushing and Grasping via Deep Reinforcement Learning and Image Masking

When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications.

Wettability regulation of the electric double-layer structural evolution of complex fluids: Insights into thermodynamic and electrical properties

Understanding the structural evolution at the electrode is essential for accurate prediction of complex fluid applications, where the carbon nanotube is chosen as the carrier of CO2-ionic liquids (ILs) in electroreduction. Then, the electrical double layer with tunable wettability is investigated by molecular dynamics simulations. The competition and cooperation between van der Waals and Coulomb interactions are evaluated by examining the structural and electric characteristics. When an external potential (φ) is initiated, the co-ions are repelled from the electrode and the counter-ions compete with CO2 in the electric double layer (EDL), with different thermodynamics produced by varying the proportion of CO2/ionic liquid. As the solid–liquid interaction parameter (β) increases, more counter-ions aggregate, producing double density peaks for Tf2N− and sharply increasing the density of CO2. With increases in β and φ, the local charge density and local field potential increase, and the EDL thickness decreases. However, the location of the CO2 density layer shifts ahead to the counter-ions, weakening their shielding effect and capacitance. Using a combination of structural analysis, the first and second peaks of Tf2N− of EDL are composed of sulfonyl and trifluoromethyl, respectively. As a response, the steric hindrance of CO2 decreases, and more molecules migrate to the surface in a parallel orientation. The structural evolution is quantitatively evaluated in terms of the entropy, results show that the orientation transition is prominent in structural evolution. The coupling relation between thermodynamic and electrical properties plays a pivotal role in determining the structural evolution of complex mixtures, and these findings could benefit the advancement of ILs-based CO2 electroreduction and other complex fluid applications.

Studying the application of a microbiological complex in sterbel growing

Abstract The main goal of the research is to establish the effect of the Bonaka-APK microbiological complex, which is a metabiotic for the management of fish autoflora and the comprehensive prevention of bacterial infections for fingerling sturgeon (sterbel). To achieve the goal, the following tasks were set: to establish the effect of Bonac-APK on fingerling sturgeon; establish the economic efficiency of its use for aquaculture. The study of the effect of the additive for growing aquaculture for yearling sturgeon fish (sterbel) was carried out with a fish weight of 130 g. The study showed that in an experiment on Sterbel the weight of fish at the end of rearing increased in experimental groups by 3.5-15.6%. The body length of fish in experimental groups increased by 1.1-2.2%. The survival rate of fish in all groups was 100%. Biochemical blood parameters of fish were within normal limits in both the control and experimental groups, which indicates that the probiotic feed additive Bonaka-APK does not have a harmful effect on the fish body. The level of lipid peroxidation increased, which indicates an increase in the antioxidant defense of the Sterbel body. The decrease in feed costs per 1 kg of weight gain compared to the control was 8.0-12.0% in experimental groups. It is recommended to use the microbiological complex Bonaka-APK in an amount of 0.4% by weight of the feed, which is a metabiotic for managing fish autoflora and complex prevention of bacterial infections for sturgeon fingerlings of the BONAKA Company.

Optimizing Key Value Indicators in Intent-Based Networks through Digital Twins aided service orchestration mechanisms

For many years, the orchestration of network resources and services has been addressed by considering homogeneous communication infrastructures and simple Service Level Agreements (SLAs), generally defined through a list of traditional Key Performance Indicators (KPIs). Unfortunately, state-of-the-art solutions risk being quite ineffective for future telecommunication systems. Beyond 5G networks, for instance, are emerging as complex and heterogeneous ecosystems where resources belonging to diverse network domains with evolving capabilities can be dynamically exposed to support much more complex and cross-domain services and applications. At the same time, SLAs will be defined by also considering novel performance demands, including security, economic, and environmental needs. Based on these premises, this work proposes a novel orchestration strategy designed to fulfill service requirements expressed through Key Value Indicators (KVIs), while combining the potentials of both Network Digital Twins and Intent-Based Networking. Leveraging insights from Network Digital Twins, multiple service orchestration options are explored to optimize resource utilization. Simultaneously, Intent-Based Networking is adopted to streamline network management via intents, specifying Beyond 5G requirements through KPIs and KVIs. An optimal orchestration scheme has been conceived through a multi-criteria decision-making algorithm and a many-to-many matching game between domains and service requests mapped into intents, aiming to minimize SLA violations over time. The performance of the conceived solution has been investigated through computer simulations in realistic scenarios. The obtained results clearly highlight its effectiveness and demonstrate that it is able to reduce SLA violations (related to latency, throughput, costs, and cyber risk requirements) by up to 22.44% compared to other baseline techniques.

Sticky Science: Using Complex Coacervate Adhesives for Biomedical Applications.

Tissue adhesives are used for various medical applications, including wound closure, bleeding control, and bone healing. Currently available options often show weak adhesion or cause adverse effects. Recently, there has been an increasing interest in complex coacervates as medical adhesives. Complex coacervates are formed by mixing oppositely charged macromolecules that associate and undergo liquid-liquid phase separation, in which the dense bottom phase is the complex coacervate. Complex coacervates are strong and often biocompatible, and show strong underwater adhesion. The properties of the resulting materials are tunable by intrinsic factors such as polymer chemistry, molecular weight, charge density, and topology of the macromolecules, as well as extrinsic factors such as temperature, pH, and salt concentration. Therefore, complex coacervates are interesting new candidates for medical adhesives. In this review, it is described how complex coacervates form and how different factors influence their behavior. Next, an overview of recent studies on complex coacervates in the context of medical adhesives is presented. The application of complex coacervates as hemostatic or embolic agents, skin or bone repair adhesives, and soft tissue sealants is discussed. Lastly, additional possibilities for utilizing these materials in the future are discussed.

Solving the Hele–Shaw flow using the Harrow–Hassidim–Lloyd algorithm on superconducting devices: A study of efficiency and challenges

The development of quantum processors for practical fluid flow problems is a promising yet distant goal. Recent advances in quantum linear solvers have highlighted their potential for classical fluid dynamics. In this study, we evaluate the Harrow–Hassidim–Lloyd (HHL) quantum linear systems algorithm (QLSA) for solving the idealized Hele–Shaw flow. Our focus is on the accuracy and computational cost of the HHL solver, which we find to be sensitive to the condition number, scaling exponentially with problem size. This emphasizes the need for preconditioning to enhance the practical use of QLSAs in fluid flow applications. Moreover, we perform shots-based simulations on quantum simulators and test the HHL solver on superconducting quantum devices, where noise, large circuit depths, and gate errors limit performance. Error suppression and mitigation techniques improve accuracy, suggesting that such fluid flow problems can benchmark noise mitigation efforts. Our findings provide a foundation for future, more complex application of QLSAs in fluid flow simulations.

Interaction between pea protein isolate and quercetin: Effects on protein conformation and quercetin activity.

Comprehensive comprehension of the interaction between proteins and polyphenols is crucial for advancing their utilization in food processing. This study investigated no-covalent interaction between pea protein isolate (PPI) and quercetin (Que) through spectroscopic analysis and molecular simulation. Fourier transform infrared spectroscopy and circular dichroism spectrum showed that the interaction between PPI and Que changed the secondary structure of the protein due to a decrease in α-helix content and an increase in the random coil. Thermodynamic parameters indicated that the Quebound PPI via hydrogen bonds and hydrophobic interactions (ΔH>0, ΔS>0, and ΔG<0), which was also confirmed by molecular docking. Particle size and ζ-potential showed that PPI and Que demonstrated effective interaction and binding capabilities, enhancing the stability. In addition, the antioxidant and bioaccessibility of complexes have also been enhanced. This study shed a light on the application of protein-polyphenol complexes for developing functional foods. PRACTICAL APPLICATION: Interaction between pea protein isolate and quercetin can change the protein conformation to maintain the stability of quercetin and is helpful to expand the market value and application value of plant protein. The research has important implications for using leguminous protein as embedded support to improve the stability of polyphenols compounds.

SYNTHESIS AND PROPERTIES OF BINARY AND TERNARY INTERPOLYELECTROLYTE COMPLEXES OF CHITOSAN

This review provides a comprehensive summary of the results of research, published from 2002 to 2023, on the synthesis, physicochemical properties and medical applications of binary and ternary interpolyelectrolyte complexes (IPECs) based on chitosan, carboxymethylcellulose and collagen. The application of IPECs in the medical field mainly refers to their use in medical therapy and bone regeneration. In addition, we describe our original studies on IPECs based on chitosan and collagen.

ChatGPT as a Digital Assistant for Archaeology: Insights from the Smart Anomaly Detection Assistant Development

The introduction of generative AI has the potential to radically transform various fields of research, including archaeology. This study explores the potential of generative AI, specifically ChatGPT, in developing a computer application for analyzing aerial and satellite images to detect archaeological anomalies. The main focus was not on developing the application itself but on evaluating ChatGPT’s effectiveness as an IT assistant for humanistic researchers. Starting with a simple prompt to analyze a multispectral orthophoto, the application was developed through successive iterations, improved through continuous interactions with ChatGPT. Various technical and methodological challenges were addressed, leading to the creation of a functional application with multiple features, including various analysis methods and tools. This process demonstrated how the use of large language models (LLMs) can break down the barriers between humanities and computer science disciplines, enabling researchers without programming skills to develop complex applications in a short time.

Multifunctional 3D pristine graphene with tunable electromagnetic properties via self-assembly strategy

Nowadays, there is an urgent demand for multifunctional materials with tunable properties to meet the requirements of intricate environments with alternating temperatures and electromagnetic conditions. Pristine graphene is a highly promising electromagnetic material owing to its intrinsic advantages. However, issues related to aggregation and weak gelation capability would hinder the fabrication and advancement of multifunctional pristine graphene. Herein, multifunction properties are first integrated into one 3D structured pristine graphene via a self-assembly method. By regulating the microstructures, 3D structured pristine graphene can exhibit a range of electromagnetic properties, including wave transmission (wave transmittance of 88 %-93 %), electromagnetic wave (EMW) absorption (reflection loss of −50.70 dB), and electromagnetic interference (EMI) shielding (shielding effectiveness (SE) of ∼37.90 dB), which are not available in existing graphene materials. Furthermore, the 3D structured pristine graphene exhibits mechanical stability and thermal performances to guarantee a stable and durable electromagnetic response for complex environmental applications. The outstanding multifunction can be attributed to the low defects and controllable microstructures of pristine graphene. This work supplies an inspiration for the development of multifunctional graphene materials as well as the microstructure design of 3D pristine graphene.

A novel bistable optical phase shifter using non-volatile MEMS actuator for application in photonic integrated circuits

Recently, micro-electromechanical systems (MEMS) actuators have been employed to perform the tuning mechanisms in photonic integrated circuits (PICs). Photonic components with MEMS-based tuning mechanisms offer several advantages, including small dimensions, low power consumption, fast tuning time, and high compatibility with silicon photonic platforms. These features enable the integration of photonic components on a single chip, which is demanded for complex applications such as artificial intelligence applications including machine learning, neural networks, and neuromorphic computing. Phase shifters are one of the most important components in PICs. This paper introduces a novel bistable optical phase shifter that utilizes a MEMS-based tuning mechanism to maintain its last state without consuming electrical power. The proposed device boasts impressive functional characteristics including a phase shift of 2.32π, a tuning time of 1.3μs, an optical loss of 0.03 dB, and compact dimensions of 35 μm by 35 μm while operating at a voltage of 18V. Given these remarkable features, the proposed device is an appealing candidate for integration in complex PICs such as photonics-based AI systems.

Cu-MOF nanosheets with laccase-like activity for phenolic compounds detection and dye removal

In this study, a Cu-MOF nanozyme has been synthesized using 2-methylimidazole coordinated with copper ions for detecting phenolic compounds and removing dyes. The Cu-MOF nanozyme exhibited robust laccase-like catalytic performance in alkaline conditions, high temperature, and under high salt concentrations. Acceptable selectivity and affinity (Km = 0.14 mM) were observed for the 2,4-dichlorophenol (2,4-DP) substrate in the Cu-MOF nanozyme, allowing for its application in the quantitative detection of 2,4-DP. The limit of detection was determined to be 9.0 × 10−4 mM with a linear range of 2.5 × 10−3–8.0 × 10−2 mM. Finally, the colorimetric method developed was employed for detecting 2,4-DP in two water samples with satisfactory spiked recoveries (88.7 %–95.9 %). Importantly, the synthesized Cu-MOF nanozyme can efficiently remove two organic dyes (malachite green and congo red) simultaneously, and the developed system is able to cope with complex real-world applications where wastewater contains multiple organic dyes. The Cu-MOF nanozyme, with its simple synthesis, low cost, high catalytic activity, good stability, and high substrate selectivity, is a promising candidate for water pollution treatment and rapid detection. In addition, the present study introduces a versatile approach for mimicking the catalytic center of biological enzymes by combining 2-methylimidazole with metal ions.

An Additively Optimal Interpreter for Approximating Kolmogorov Prefix Complexity.

We study practical approximations of Kolmogorov prefix complexity (K) using IMP2, a high-level programming language. Our focus is on investigating the optimality of the interpreter for this language as the reference machine for the Coding Theorem Method (CTM). This method is designed to address applications of algorithmic complexity that differ from the popular traditional lossless compression approach based on the principles of algorithmic probability. The chosen model of computation is proven to be suitable for this task, and a comparison to other models and methods is conducted. Our findings show that CTM approximations using our model do not always correlate with the results from lower-level models of computation. This suggests that some models may require a larger program space to converge to Levin's universal distribution. Furthermore, we compare the CTM with an upper bound on Kolmogorov complexity and find a strong correlation, supporting the CTM's validity as an approximation method with finer-grade resolution of K.

Recent advances in complementary label learning

Complementary Label Learning (CLL), a crucial aspect of weakly supervised learning, has seen significant theoretical and practical advancements. However, a comprehensive review of the field has been lacking. This survey provides the first exhaustive compilation and synthesis of state-of-the-art CLL approaches, filling a critical gap in the literature and serving as a foundational resource for the community. Key contributions of this survey include an extensive categorization of CLL methodologies, clarifying diverse algorithms based on their principles and applications. This classification scheme enhances understanding of the CLL landscape, highlighting its versatility across varied settings. Additionally, the survey examines the evolution of CLL, showcasing its adaptability and potential in addressing complex applications. It also explores experimental frameworks, including processes for generating complementary labels and datasets and numerical evaluation of existing state-of-the-art. Moreover, the survey delves into how CLL integrates with and enhances other weakly supervised and semi-supervised learning approaches, deepening understanding of its role in the broader machine learning ecosystem. Overall, this survey not only compiles CLL research but also guides future explorations, steering the field towards new horizons in weakly supervised learning.