Evolution Technique Research Articles

Exposing the dead-cone effect of jet quenching in QCD medium

Abstract When an energetic parton traverses the hot QCD medium it may suffer multiple scattering and lose its energy. The medium-induced gluon radiation for a massive quark will be suppressed relative to that of a light quark due to the dead-cone effect. The development of new declustering techniques of jet evolution makes a direct study of the dead-cone effect in the QCD medium possible for the first time. In this work, we compute the emission angle distribution of the charm-quark initiated splittings in $\rm D^0$ meson tagged jet and that of the light parton initiated splittings in an inclusive jet in p+p and Pb+Pb at $\rm 5.02$~TeV by utilizing the declustering techniques of jet evolution. The heavy quark propagation and indued energy loss in the QCD medium are simulated with the SHELL model based on the Langevin equation. By directly comparing the emission angle distributions of charm-quark-initiated splittings with those of light parton-initiated splittings at the same energy intervals of the initial parton, we provide insights into the fundamental splitting structure in A+A collisions， thereby exploring the possible exposure of the dead-cone effect in medium-induced radiation. We further investigate the case of the emission angle distributions normalized to the number of splittings and find the dead-cone effect will broaden the emission angle of the splitting and reduce the possibility to occur such splitting, therefore leading the massive parton to lose less energy. We also find the collisional energy loss mechanism has a negligible impact on the medium modification to the emission angle distribution of the charm-quark initiated splittings for $\rm D^0$-meson tagged jets. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd

Key frame extraction algorithm for surveillance videos using an evolutionary approach

With rapid technological advancements, videos are captured, stored, and shared in multiple formats, increasing the requirement for summarization techniques to enable shorter viewing durations. Key Frame Extraction (KFE) algorithms are crucial in video summarization, compression, and offline analysis. This study aims to develop an efficient KFE approach for generic videos. Existing methods include the Adaptive Key Frame Extraction Algorithm, which reduces redundancy while ensuring maximum content coverage; the Optimal Key Frame Extraction Algorithm, which utilizes a Genetic Algorithm (GA) to select key frames optimally; and the Rapid Key Frame Extraction Algorithm, which employs clustering techniques to identify typical key frames. However, a clear prerequisite remains for a more versatile KFE technique that can address generic applications rather than specific use cases. Evolutionary algorithms offer a powerful solution for achieving optimal KFE. This proposed method leverages an interactive GA with a well-designed Fitness Function and elitism-based survivor selection to enhance performance. This proposed algorithm has been tested on diverse datasets, including VSUMM, SumMe, Mall, user-generated videos, surveillance footage from Amrita Vishwa Vidyapeetham University (Coimbatore, India), and web-sourced videos. The results demonstrate that the proposed KFE approach adheres to benchmark data and captures additional significant frames. Compared to Differential Evolution (DE) techniques and Deep Learning (DL) models from the literature, this recommended algorithm demonstrates superior efficiency, as verified through quantitative and qualitative evaluation metrics. Furthermore, the computational complexity of the GA is intricately compared to that of DE and DL-based approaches, highlighting the distinct efficiencies and performance features.

Leveraging fuzzy embedded wavelet neural network with multi-criteria decision-making approach for coronary artery disease prediction using biomedical data

Coronary artery disease (CAD) is the main cause of death. It is a complex heart disease that is linked with many risk factors and a variety of symptoms. In the past few years, CAD has experienced a remarkable growth. Prompt risk prediction of CAD would be capable of decreasing the death rate by permitting timely and targeted treatments. Angiography is the most precise CAD diagnosis technique; however, it has several side effects and is expensive. Multi-criteria decision-making approaches can well perceive CAD by analysing main clinical indicators like ChestPain type, ST_Slope, and HeartDisease presence. By assessing and evaluating these factors, the model improves diagnostic accuracy and aids informed clinical decisions for quick CAD detection. Mainly machine learning (ML) and deep learning (DL) use plentiful models and algorithms, which are commonly employed and very useful in exactly detecting the CAD within a short time. Current studies have employed numerous features in gathering data from patients while using dissimilar ML and DL models to attain results with high accuracy and lesser side effects and costs. This study presents a Leveraging Fuzzy Wavelet Neural Network with Decision Making Approach for Coronary Artery Disease Prediction (LFWNNDMA-CADP) technique. The presented LFWNNDMA-CADP technique focuses on the multi-criteria decision-making model for predicting CAD using biomedical data. In the LFWNNDMA-CADP method, the data pre-processing stage utilizes Z-score normalization to convert an input data into a uniform format. Furthermore, the improved ant colony optimization (IACO) method is used for electing an optimum sub-set of features. Furthermore, the classification of CAD is accomplished by utilizing the fuzzy wavelet neural network (FWNN) technique. Finally, the hyperparameter tuning of the FWNN model is accomplished by employing the hybrid crayfish optimization algorithm with the self-adaptive differential evolution (COASaDE) technique. The simulation outcomes of the LFWNNDMA-CADP approach are investigated under a benchmark database. The experimental validation of the LFWNNDMA-CADP approach portrayed a superior accuracy value of 99.49% over existing techniques.

Advanced transdermal drug delivery system: A comprehensive review of microneedle technologies, novel designs, diverse applications, and critical challenges.

Transdermal drug delivery presents numerous advantages over conventional administration routes, including non-invasiveness, enhanced patient adherence, circumvention of hepatic first-pass metabolism, self-administration capabilities, controlled release, and increased bioavailability. Nevertheless, the barrier function of stratum corneum limits this strategy to molecules possessing requisite physicochemical attributes. To expand the field of transdermal delivery, researchers have pioneered physical enhancement techniques, with micron-sized needles emerging as a particularly promising platform for the transdermal and intradermal delivery of therapeutic agents across a spectrum of molecular sizes. Microneedles function by disrupting the skin's integrity, generating microchannels that facilitate efficient drug permeation. This innovative technology boasts a captivating profile characterized by non-invasive drug delivery, enhanced efficacy and onset time, improved patient acceptability, self-administration possibilities, and precise dosing capabilities. Consequently, both academic institutions and industry have invested substantial resources in the development of microneedle systems for pharmaceutical delivery. This comprehensive review elucidates the multifaceted aspects of microneedle technology, encompassing its historical evolution, diverse materials, innovative designs, fabrication methodologies, and characterization techniques. The review extends to various microneedle types, including solid, hollow, coated, dissolving, swelling, and porous microneedles, as well as cutting-edge designs such as stimulus-responsive, iontophoresis-assisted, and bionic microneedles. Furthermore, we explore microneedle applications in vaccination, targeted delivery, and the administration of biologics, long-acting therapeutic agents, and cosmetics. Critical challenges in microneedle development, including dimensional considerations, safety concerns, acceptability factors, production scalability, regulatory hurdles, and sustainability issues, are thoroughly addressed, alongside a presentation of future prospects in this rapidly evolving field.

Optimisation strategies for directed evolution without sequencing.

Directed evolution can enable engineering of biological systems with minimal knowledge of their underlying sequence-to-function relationships. A typical directed evolution process consists of iterative rounds of mutagenesis and selection that are designed to steer changes in a biological system (e.g. a protein) towards some functional goal. Much work has been done, particularly leveraging advancements in machine learning, to optimise the process of directed evolution. Many of these methods, however, require DNA sequencing and synthesis, making them resource-intensive and incompatible with developments in targeted in vivo mutagenesis. Operating within the experimental constraints of established sorting-based directed evolution techniques (e.g. Fluorescence-Activated Cell Sorting, FACS), we explore approaches for optimisation of directed evolution that could in future be implemented without sequencing information. We then expand our methods to the context of emerging experimental techniques in directed evolution, which allow for single-cell selection based on fitness objectives defined from any combination of measurable traits. Finally, we explore these alternative strategies on the GB1 and TrpB empirical landscapes, demonstrating that they could lead to up to 19-fold and 7-fold increases respectively in the probability of attaining the global fitness peak.

On the Use of DELEqC-III in Bilevel Problems with Linear Equality Constraints

The bilevel programming problem (BLP) is an optimization problem with another optimization problem in its constraints. This framework finds utility in modeling decentralized scenarios, which arise in real-world applications such as traffic management, transportation, and economic policy. Differential Evolution (DE) techniques have emerged in literature for addressing such complex problems. However, handling linear equality constraints poses a significant challenge for DE and other metaheuristics. To address this issue, we previously introduced DELEqC, enhancing DE with a mechanism to manipulate the linear equality constraints. A specialized variant, BL-DELEqC, was further proposed specifically for tackling general BLPs. Another variant, DELEqC-III, transforms the original constrained optimization problem into a lower-dimensional unconstrained one, offering applicability to BLPs with linear equality constraints. Thus, we explore in this study the efficacy of DELEqC-III in handling BLPs with linear equality constraints. The proposed BL-DELEqC-III is compared to BL-DELEqC on a selection of benchmark BLPs, demonstrating superior results.

An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization

Global optimization is critical in engineering, computer science, and various industrial applications as it aims to find optimal solutions for complex problems. The development of efficient algorithms has emerged from the need for optimization, with each algorithm offering specific advantages and disadvantages. An effective approach to solving complex problems is the hybrid method, which combines established global optimization algorithms. This paper presents a hybrid global optimization method, which produces trial solutions for an objective problem utilizing a genetic algorithm’s genetic operators and solutions obtained through a linear search process. Then, the generated solutions are used to form new test solutions, by applying differential evolution techniques. These operations are based on samples derived either from internal line searches or genetically modified samples in specific subsets of Euclidean space. Additionally, other relevant approaches are explored to enhance the method’s efficiency. The new method was applied on a wide series of benchmark problems from recent studies and comparison was made against other established methods of global optimization.

Structurally-Modulated Substrate of MXene for Surface-Enhanced Raman Scattering Sensing.

The distinctive structure of MXene offers exceptional electron transport properties, abundant surface chemistry, and robust mechanical attributes, thereby bestowing it with remarkable advantages and promising prospects in the realm of surface-enhanced Raman scattering (SERS). This review comprehensively outlines the evolution, synthesis methodologies, and characterization techniques employed for MXene-based SERS substrates. It delves into the intricacies of its SERS enhancement mechanism, substrate variants, and performance metrics, alongside showcasing its diverse applications spanning molecular detection, biosensing, and environmental monitoring. Furthermore, it endeavors to pinpoint the research bottlenecks and chart the future research trajectories for MXene-based SERS substrates.

Marine predator’s algorithm-based linear and elliptical antenna array design for 5G communication

The highly efficient linear and elliptical antenna array design presented in this article is intended to synthesize far-field radiation patterns for 5G communication. The objective is to achieve the best possible radiation pattern, which includes a lower side lobe level (SLL) and a narrower half-power beam width (HPBW). A low SLL is crucial to minimize intervention in the entire side lobe region, whereas long-distance communication requires a low HPBW. The marine predator’s algorithm (MPA) is used here to determine the optimal feeding currents to each array element to reduce the SLL and HPBW values. Two linear antenna array (LAA) design examples and the three elliptical antenna array (EAA) design examples are discussed in this article which are achieved using the optimal current excitations to each array element. Two well-established evolutionary optimization techniques, namely the genetic algorithm (GA) and the differential evolution (DE) technique, are also employed for comparison to demonstrate the superiority of the MPA optimization-based technique. The results achieved utilizing the MPA show the improvement of SLL contraction compared to the uniform antenna array and the methods affirmed in the recently published article.

The superiority of feasible solutions-moth flame optimizer using valve point loading

The optimal power flow (OPF) problem deals with large-scale, nonlinear, and non-convex optimization challenges, often accompanied by stringent constraints. Apart from the primary operational objectives of an energy system, ensuring load bus voltages remain within acceptable ranges is essential for providing high-quality consumer services. The Moth-Flame Optimizer (MFO) method is inspired by the unique night flight characteristics of moths. Moths, much like butterflies, undergo two distinct life stages: larval and mature. They have evolved the ability to navigate at night using a technique called transverse orientation. This article presents a methodology for determining the optimal energy transmission system configuration by integrating power producers. The MFO, Grey Wolf Optimizer (GWO), Success-history-based Parameter Adaptation Technique of Differential Evolution - Superiority of Feasible Solutions (SHADE-SF), and Superiority of Feasible Solutions-Moth Flame Optimizer (SF-MFO) algorithms are applied to address the OPF problem with two objective functions: (1) reducing energy production costs and (2) minimizing power losses. The efficiency of MFO, SF-MFO, SHADE-SF, and GWO for the OPF challenge is evaluated using IEEE 30-feeder and IEEE 57-feeder systems. Based on the collected data, SF-MFO demonstrated the best performance across all simulated instances. For instance, the electricity production costs generated by SF-MFO are $845.521/hr and $25,908.325/hr for the IEEE 30-feeder and IEEE 57-feeder systems, respectively. This represents a cost savings of 0.37 % and 0.36 % per hour, respectively, compared to the lowest values obtained by other comparative methods.

The Research Advances in Distant Hybridization and Gynogenesis in Fish

ABSTRACTDistant hybridization and gynogenesis are two prevalent breeding techniques for fishes. Drawing from the research achievements of our team and the existing literature, we summarize the reproductive traits and genetic features of fishes derived from distant hybridizations and gynogenesis, and we deduce the fundamental mechanisms of these two methods and compare them, discerning their common and different characteristics. Both distant hybridization and gynogenesis techniques can alter genotypes and phenotypes, thus establishing them as significant breeding methods. Additionally, the genetic principles and the basic biological characteristics of distant hybridization and gynogenesis in fish have been inferred. We propose the concepts of macro‐hybrid and micro‐hybrid based on extensive experimental findings from fish distant hybridizations and gynogenesis. The term “macro‐hybrid” refers to offspring from distant hybridization that possess two distinct subgenomes, each inherited from one of the two parental species, such as allodiploid and allotetraploid lineages. The concept of “micro‐hybrid” refers to offspring, including autodiploid and autotetraploid lineages, as well as those resulting from artificial gynogenesis, whose genome almost originates solely from the maternal parent but in which certain DNA fragments derived from the paternal parent insert. Distant hybridization and gynogenesis are vital techniques in fish genetics, breeding, and evolution. We highlight the prospective paths for research and application of distant hybridization and gynogenesis in fishes.

Biocatalysis in Non-Conventional Media: Unlocking the Potential for Sustainable Chiral Amine Synthesis.

The application of biocatalysis has become essential in both academic and industrial domains for the asymmetric synthesis of chiral amines, and it serves as an alternative tool to transition-metal catalysis and complements traditional chemical methods. It relies on the swift expansion of available processes, primarily as a result of advanced tools for enzyme discovery, combined with high-throughput laboratory evolution techniques for optimizing biocatalysts. This concept paper explores the utilization of non-conventional media such as ether-type solvents, deep eutectic solvents, and micellar catalysis to enhance biocatalytic reactions for chiral amine synthesis. Each section focuses on the unique properties of these media, including their ability to stabilize enzymes, alter substrate solubility, and modulate enzyme selectivity. The paper aims to provide insights into how these innovative media can overcome traditional limitations, offering new avenues for sustainable and efficient chiral amine production through biocatalytic processes.

Biodegradable annuloplasty rings: evolution, characteristics and surgical technique

Annulus remodeling and stabilization with a ring have been demonstrated to be a mandatory step in mitral and tricuspid valve repair to ensure effective leaflet coaptation and improve long-term results. The need for a degradable ring stems from two areas in which biodegradable ring annuloplasty can theoretically fill a void: 1 – valve repair in infected valve tissue to avoid colonization of permanent implanted ring device and infection recurrence, 2 – valve repair in children, where traditional annuloplasty rings can’t be used due to the risk of acquired stenosis for lack of growth. Prophylactic anticoagulation following biodegradable ring annuloplasty is not necessary, provided that the patient has no other indication for it, as the intraannular position of the ring protects against any «blood-biodegradable ring» interaction.

Optimized differential evolution and hybrid deep learning for superior drug-target binding affinity prediction

Investigating Drug-Target Interactions (DTI) is crucial for drug repositioning and discovery tasks. However, discovering DTIs through experimental approaches is time-consuming and requires substantial financial resources. To address these challenges, machine learning-based methodologies have been adopted to reduce costs and save time. Unfortunately, the effectiveness of these methods has been limited due to the binary classification approach and the lack of empirically validated negative samples. The availability of abundant DTI datasets and protein structure data has enabled the development of new approaches, such as redefining the DTI problem as a regression task. Given this context, we propose an innovative deep-learning approach to predict binding affinities between drugs and targets. Our model, named the Convolution Self-Attention Network with Attention-based Bidirectional Long Short-Term Memory Network (CSAN-BiLSTM-Att), integrates convolutional neural network (CNN) blocks with self-attention mechanisms to create an attention-based bidirectional long short-term memory (BiLSTM) model, followed by fully connected layers. Due to the model's complexity, proper hyperparameter tuning is essential. To optimize this, we employ the Differential Evolution (DE) technique to select the most suitable hyperparameters. Experimental results demonstrate that the DE-based CSAN-BiLSTM-Att model outperforms previous approaches. Specifically, the model achieved a concordance index of 0.898 and a mean square error of 0.228 on the DAVIS dataset, and a concordance value of 0.971 with a mean square error of 0.014 on the KIBA dataset.

Service restoration in distribution systems considering priority customers and microgrids

Service restoration (SR) consists of automatically generating and executing a plan to restore the service in healthy zones using the least number of maneuvers after detecting and isolating a permanent fault in the distribution system zone. This component is essential to self-healing functionality in smart grids and allows customers to reconnect quickly to the distribution grid after a power outage. Distributed generation (DG) supports the distribution network when there is insufficient capacity to restore all zones out of service or supply the loads locally through microgrids. The power supply must be restored to the highest priority customers in case of partial restoration. Also, most research works use simplified or linearized models to propose restoration algorithms. This paper proposes a complete AC formulation for the service restoration problem in distribution systems considering network reconfiguration (NR), the integration of distributed generation (DG), and priority customers (PCs) into the solution. The optimization problem is solved by a centralized algorithm based on combining the Differential Evolution (DE) and Continuous Population-Based Incremental Learning (PBILc) metaheuristics techniques. Simulation results are presented for three case studies in which the IEEE 33-bus distribution system is tested for different fault scenarios. The numerical results show the robustness and efficiency of the proposed algorithm.

Endoscopic interventions in pancreatic strictures and stones-A structured approach.

Chronic pancreatitis (CP) is an irreversible disease of varied etiology characterized by destruction of pancreatic tissue and loss of both exocrine and endocrine function. Pain is the dominant and most common presenting symptom. The common cause for pain in CP is ductal hypertension due to obstruction of the flow of pancreatic juice in the main pancreatic duct either due to stones or stricture or a combination of both. With advances in technology and techniques, endoscopic retrograde cholangiography (ERCP) and stenting should be the first line of therapy for strictures of the main pancreatic duct (MPD). Small calculi in the MPD can be extracted by ERCP and balloon trawl. Extracorporeal shockwave lithotripsy (ESWL) remains the standard of care for large pancreatic calculi and aims to fragment the stones 3mm or less that can easily be extracted by a subsequent ERCP. Single operator pancreatoscopy with intraductal lithotripsy is a technique in evolution and can be tried when ESWL is not available or is unsuccessful in producing stone fragmentation.

Bimodal Trending in Corrosion Loss of Magnesium Alloys

Data for the mass loss of a variety of magnesium alloys as a function of an exposure period show that corrosion loss follows bimodal trending with time for different exposure environments, with both laboratory and field supporting these findings. For datasets sufficient to discriminate bimodal behavior, the instantaneous rate of corrosion at the commencement of the second mode is (close to) four times the instantaneous rate of corrosion at the end of the first mode (i.e., through the transition period). This observation is consistent with the theoretical relative diffusivities of oxygen and hydrogen through the corrosion product layer as it exists during the transition period. These findings support the notion that the bimodal model has corrosion in mode 1 rate-controlled by the cathodic oxygen reduction reaction and the inward diffusion of oxygen while in mode 2 corrosion is rate-controlled by the cathodic hydrogen evolution reaction and the outward diffusion of hydrogen. Similar findings have been made previously for various ferrous and other alloys and thus throws new light on the development of corrosion of magnesium alloys. It also provides reasons for measurements of hydrogen evolution and electrochemical techniques underestimating magnesium corrosion rates. A new procedure for combining these is proposed.

An advanced kernel search optimization for dynamic economic emission dispatch with new energy sources

Integrating renewable energy sources, such as wind and photovoltaic power generation, into the power grid is crucial for sustainable power system development and mitigating pollutant emissions. However, these sources’ inherent uncertainty and randomness pose significant challenges to grid operations. Metaheuristic algorithms offer efficient solutions to optimization problems in this context and are widely employed in practice. Kernel Search Optimization (KSO) has emerged as a prominent metaheuristic algorithm due to its parameter-free nature and applicability to power dispatch problems. Nevertheless, KSO’s limited local search capabilities necessitate enhancements for improved performance. This paper introduces an enhanced variant of KSO, termed Upgraded Kernel Search Optimization (UKSO), which incorporates differential evolution techniques, including mutation, crossover, and selection mechanisms, to bolster KSO’s search capabilities and overall performance. The efficacy and feasibility of UKSO are evaluated through comprehensive testing using the CEC2017 benchmark. Comparative analysis demonstrates that UKSO outperforms other algorithms by achieving more optimal solutions. To address power dispatch challenges in the context of renewable energy sources, a Two-Time Energy Dispatch (TTED) model is proposed, leveraging a weighted summation approach to minimize total fuel costs and pollution emissions simultaneously. The application of the Prophet model, based on Bayesian fitting, facilitates accurate prediction of wind and photovoltaic power outputs. Through experimentation across small (8 and 14 units) and large (58 units) systems, the feasibility of UKSO and TTED is validated. In complex scenarios, UKSO demonstrates a 0.69% to 3.64% reduction in pollution emissions and a 0.99% to 1.23% decrease in economic costs compared to KSO under varying weight configurations. Furthermore, UKSO achieves a renewable energy generation utilization rate exceeding 90%, emphasizing its ability to iterate efficiently through complex problems and its efficacy in addressing renewable power dispatch challenges. These results highlight the practical significance of UKSO in optimizing power systems and provide valuable insights for future research and applications in this domain.

An analytical method for reliability evaluation of power distribution system with time-varying failure rates

Reliability evaluation is crucial in assessing the power supply capacity and guiding the planning and operation of the distribution system. Traditional analytical reliability evaluation usually assumes a constant failure rate for components, implying that the life distribution follows an exponential distribution. However, in reality, the failure rate of components varies over time due to external disturbances and aging. As a result, traditional analytical reliability evaluation methods are no longer applicable. To address this issue, this paper presents an analytical method for evaluating the reliability of the distribution system with time-varying failure rates. The method considers short-term and long-term time-varying failure rates using density evolution and non-homogeneous Poisson process techniques, respectively. Both steady-state and instantaneous indices are calculated to reflect the level of system reliability. Finally, the proposed method is applied to the IEEE RBTS Bus 6 test system, demonstrating its correctness and efficiency.

Robust optimal dispatching of power grid with the participation of virtual power plants

Abstract One of the key technologies for creating intelligent distribution networks is energy management systems, which allow virtual power plants (VPPs) to combine distributed flexible resources and accomplish unified scheduling and coordination. This paper presents a solid optimization model that compromises safety and economics for distribution networks involving virtual power plants. First, a power flow model for the distribution network is created to minimize voltage variation, operating cycle cost, and the safety operation of several AM (active management) methods as constraints. Second, a strong optimization model for the distribution network was built with the participation of virtual power plants, accounting for the unpredictability of the load and fresh energy. Finally, the problem was solved using the relaxation evolution technique, and the precision and potency of the suggested model were shown by doing a simulated comparison study on the upgraded IEEE33 node testing system.