Evolutionary Computation for Dynamic Optimization Problems

Many real-world optimization problems are subject to dynamic environments, where changes may occur over time regarding optimization objectives, decision variables, and/or constraint conditions. Such dynamic optimization problems (DOPs) are challenging problems due to their nature of difficulty. Yet, they are important problems that researchers and practitioners in decision-making in many domains need to face and solve. Evolutionary computation (EC) encapsulates a class of stochastic optimization methods that mimic principles from natural evolution to solve optimization and search problems. EC methods are good tools to address DOPs due to their inspiration from natural and biological evolution, which has always been subject to changing environments. EC for DOPs has attracted a lot of research effort during the last two decades with some promising results. However, this research area is still quite young and far away from well-understood. This tutorial provides an introduction to the research area of EC for DOPs and carry out an in-depth description of the state-of-the-art of research in the field. The purpose is to (i) provide detailed description and classification of DOP benchmark problems and performance measures; (ii) review current EC approaches and provide detailed explanations on how they work for DOPs; (iii) present current applications in the area of EC for DOPs; (iv) analyse current gaps and challenges in EC for DOPs; and (v) point out future research directions in EC for DOPs

Development of biotechnology for recycling and reuse of wool blended textile waste materials

The rapid growth of textile consumption demands greater use of resources and enormous amounts of energy and water for producing virgin materials and processing into textiles. This results in the depletion of natural non-renewable resources and contributes significantly to carbon emissions, which is unsustainable. The new challenge facing the global textiles industry is to develop technologies for upcycling, recycling, and reuse of textile waste to achieve textile circularity. Blended fabrics have proved difficult to recycle due to fibres being intimately blended and the lack of innovation to enable effective separation of different fibre components, so blended textiles waste often end up in either landfill or incineration. Enzyme-based biotechnology has demonstrated its potential to provide innovative solutions to improve textile performance properties and reduce the negative impact of textile production on the environment. In this current research, enzyme-based biotechnology processes were explored for recycling and reuse of wool/synthetics and wool/bast fibre blended fabrics from post-consumer and/or manufacturing waste streams. Individual fibre components were separated and recovered for re-processing back into yarns for fabric production. Bast fibres such as flax and hemp fibres are regarded as sustainable fibres for textiles due to requiring almost no water or pesticides during cultivation. Recycling and reuse of bast fibres from waste textile materials could not only contribute towards diverting land use for other types of farming, saving energy and water from processing, and also meet the increasing demand for the supply of bast fibres for different sectors. The research work also demonstrates the potential to recover dyes from waste textiles and their reuse for textile coloration. These research outcomes demonstrate potential opportunities to reduce the environmental impact of textile production and support the global textile industry transition to a circular system

Application of Laser Spectroscopy and Machine Learning for Diagnostics of Uncontrolled Type 2 Diabetes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Diabetes, a chronic metabolic disorder affecting millions worldwide, presents a persistent need for reliable and non-invasive diagnostic techniques. Here, we suggest a highly effective approach for differentiating between fingernails from diabetic individuals and those from healthy controls using laser-induced breakdown spectroscopy (LIBS). The excitation source employed was a Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser emitting light with a wavelength of 1064  nm. The initial differentiation between individuals with and without diabetes was achieved by applying principal component analysis (PCA) to LIBS spectral data, which was then incorporated into a novel machine-learning model. The classification model designed for a non-invasive system included random forest (RF), an extreme learning machine (ELM) classifier, and a hybrid classification model incorporating cross-validation techniques to evaluate the outcomes. The algorithm analyses the complete spectrum of both healthy and diseased samples, categorizing them according to differences in LIBS spectral intensity. The classification performance of the model was assessed using a k-fold cross-validation method. Seven parameters, i.e., specificity, sensitivity, area under curve (AUC), accuracy, precision, recall, and F-score, were used to evaluate the model's overall performance. The findings affirmed that the suggested non-invasive model could predict diabetic diseases with an accuracy of 95%

A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping

open access articleMalicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous both to companies and to individuals. They can be hosted on various technologies and serve an array of content, including malware, command and control and complex phishing sites that are designed to deceive and expose. Tracking, blocking and detecting such domains is complex, and very often it involves complex allowlist or denylist management or SIEM integration with open-source TLS fingerprinting techniques. Many fingerprinting techniques, such as JARM and JA3, are used by threat hunters to determine domain classification, but with the increase in TLS similarity, particularly in CDNs, they are becoming less useful. The aim of this paper was to adapt and evolve open-source TLS fingerprinting techniques with increased features to enhance granularity and to produce a similarity-mapping system that would enable the tracking and detection of previously unknown malicious domains. This was achieved by enriching TLS fingerprints with HTTP header data and producing a fine-grain similarity visualisation that represented high-dimensional data using MinHash and Locality-Sensitive Hashing. Influence was taken from the chemistry domain, where the problem of high-dimensional similarity in chemical fingerprints is often encountered. An enriched fingerprint was produced, which was then visualised across three separate datasets. The results were analysed and evaluated, with 67 previously unknown malicious domains being detected based on their similarity to known malicious domains and nothing else. The similarity-mapping technique produced demonstrates definite promise in the arena of early detection of malware and phishing domains

(Dys)regulation of the Immune System in Parkinson's Disease: Methodologies, Techniques, and Key Findings from Human Studies

open access articleParkinson’s disease (PD) is the second most common neurodegenerative disorder, characterized by the degeneration of dopaminergic neurons in the midbrain. While PD is typically considered a disorder primarily affecting the central nervous system, there is mounting evidence of cellular dysfunction and PD pathology occurring in the peripheral nervous system, likely preceding central manifestations. In this context, it has become increasingly evident that dysregulation of both the central and the peripheral immune system plays a key role in PD pathogenesis and progression. In this narrative review, we describe and discuss the methodological approaches employed in human studies to investigate immune responses in PD pathogenesis and progression, their main findings and the potential to unveil novel therapeutic avenues. In particular, we present methodologies employed in and insights gained from human genetic studies, techniques utilized to investigate neuroinflammatory processes in post-mortem and living human brains, to investigate the blood-brain barrier, as well as the involvement of peripheral T cells and innate immune cells. Additionally, we elucidate methodologies utilized to explore the roles of mitochondrial dysfunction and infectious diseases in PD. Finally, we address the causes behind conflicting findings in the published literature, which may stem from disparities in sample ascertainment schemes, immunological protocols, and analysis designs. Given these challenges, it becomes imperative to develop methodological guidelines to enhance the validity of immunological studies in PD and facilitate their translation into clinical medicine

Integrating Citation Heterogeneity to Measure the Quality of Academic Journals

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Evaluating the quality of academic journals is important and complex. The journal impact factor (IF), which is the most widely used indicator to measure the quality of academic journals, assumes that all citations are homogeneous. The use of this indicator has been criticized widely due to its inherent limitations. In recent years, several sophisticated indicators have been proposed to allow the weighting of citations from different journals. However, the recursive computation process of these indicators requires a huge amount of data. This article proposes a new indicator with citation heterogeneity to measure journal quality, which is named the Citation Author Affiliation Index (CAAI). The CAAI is based on the assumption that citing paper authors’ institutions can be ranked and are considered a proxy to measure the quality of citations (in a statistical sense). It is shown that the CAAI is easy to use and interpret, time-efficient, and adaptable. The effectiveness of the CAAI is validated by using Web of Science citation data from journals in several research categories

A contribution-driven weighted grey relational analysis model and its application in identifying the drivers of carbon emissions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A number of Grey Relational Analysis (GRA) models have been developed, but their practical application could yields inconsistent or contradictory results in some situations, complicating decision-making. To address this issue, the framework for determining the Core Model Confidence Set in Grey Relational Analysis (Core GRA-MCS) is presented, and a contribution-driven weighted GRA (CDWGRA) model is proposed. First, the concept of the stability coefficient of GRA models is introduced based on the Kendall coefficient (KC). This stability coefficient quantifies the consistency of the set in system analysis. Next, a framework for determining the Core GRA-MCS is established. This framework uses the stability coefficient, Borda count, and Deng's grey relational degree to identify a subset of GRA models that reliably represent the system's characteristics. For the models in Core GRA-MCS, a weighted aggregation is performed using Deng's grey relational degree as the weight, forming the CDWGRA model. The model provides a unified approach to synthesizing results from multiple GRA models. Finally, the proposed model is used to identify the drivers of carbon emissions in the Yellow River Basin, China. The analysis identifies six key driving factors: Primary Industry, Tertiary Industry, Urbanization Rate, Urban Disposable Income, Natural Gas consumption, and Primary Electricity and Other Energy. These factors highlight the influence of economic activity, energy structure, industrial structure, and social development on regional carbon emissions. The comparative analysis and stability analysis show that the CDWGRA model improves the consistency and reliability of GRA-based analysis, confirming its validity and utility in studying complex systems

Feasible regions identification based on historical solutions for constrained optimization problems

The presence of constraints often leads to the formation of narrow and fragmented feasible regions within the search region, presenting significant challenges for optimization problem-solving. This paper introduces a novel approach, Feasible Regions Identification based on Historical Solutions (FRIHS), designed to address these challenges. FRIHS leverages previously evaluated solutions to partition the search region into ε-feasible and ε-infeasible regions. Additionally, by analyzing the correlations among constraints, they are reformulated as auxiliary objectives, effectively transforming the constrained optimization problem into a constrained multi-objective optimization problem. The method employs the classical evolutionary algorithm Differential Evolution and the multi-objective method NSGA-III to search the most promising feasible regions. The effectiveness of FRIHS is evaluated through a comparative analysis with five advanced constraint-handling algorithms across a benchmark test suite. Experimental results indicate that the proposed approach demonstrates competitive performance on the test problems

Examining the Impact of Multilevel Courtyards in Hot-Dry and Humid Climates

open access articleUrbanisation has significantly transformed human settlements, presenting sustainability challenges, particularly in hot-dry and humid climates. The urban heat island effect and increased energy consumption exacerbate reliance on mechanical cooling and fossil fuels. As climate change escalates, developing sustainable architectural solutions that improve thermal performance and energy efficiency becomes crucial. This study examines the effects of various multilevel courtyard designs on building performance in Abuja, Nigeria, highlighting gaps in applying traditional principles to these models. A mixed-method approach, combining quantitative and qualitative techniques, assesses user perceptions, thermal performance, energy efficiency, and daylighting in multilevel courtyards. Findings indicate that optimised multilevel courtyard configurations yield a 2.15 °C reduction in temperature, enhancing indoor thermal comfort and improving natural ventilation. Users favour multilevel courtyard housing; however, challenges include inadequate daylighting on lower levels and the need for shading solutions. Compressed earth blocks exhibit better thermal performance, reducing peak temperatures by 1.19 °C compared to hollow concrete blocks. Guidelines for architects and urban planners are provided, as well as recommendations for future research on policy incentives to promote multilevel courtyard models