A nonlinear mixed-frequency grey prediction model with two-stage lag parameter optimization and its application

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.With the advancement of data science, the demand for methods capable of simultaneously processing and utilizing complex mixed-frequency data systems with uncertainty characteristics is increasing. To address this need, a novel nonlinear mixed-frequency grey prediction model with two-stage lag parameter optimization is proposed which integrates frequency-domain analysis and optimization algorithm. The proposed model innovatively incorporates the phase spectrum analysis method into the mixed-frequency modeling framework, determines a reasonable range for lag parameters using frequency-domain analysis, and enhances the characterization of system nonlinearity by introducing a power-driven term. The effectiveness and robustness of the proposed model are validated through both experiments on synthetic data and real-world case studies on electricity consumption. Comparative experiments against existing mixed-frequency grey prediction model, nonlinear grey prediction model, and mixed-frequency sampling regression model demonstrate that proposed model exhibits superior performance in key metrics, including mean absolute percentage error and standard deviation. This study provides a novel solution for modeling relationships among multi-frequency variables in complex systems

A molecular perspective on horticultural trade: lessons from India forinternational species authentication

open access articleThe global horticultural industry faces increasing challenges related to species authentication, regulatory compliance, and trade transparency. Misidentification of plant species can result in economic losses, compromised consumer safety, and breaches of the regulatory framework. Traditional authentication methods, including morphological and chemical analyses, often lack the accuracy and reproducibility required for reliable identification. This review evaluates the potential of DNA barcoding as a robust molecular tool for species authentication in the horticultural trade. Drawing on case studies involving economically important plant species, we highlight the advantages of DNA-based authentication over conventional approaches. We also discuss current market regulations, limitations of molecular techniques, and the need for standardised protocols to support industry adoption. Our analysis underscores the importance of integrating DNA barcoding into global trade policies to enhance quality assurance, prevent adulteration, and ensure compliance with international standards. Future research should aim to improve cost-effectiveness, address technical challenges associated with degraded samples, and explore synergies with emerging technologies such as artificial intelligence for species identification

‘They can induce and exacerbate each other’ – the complex interplay between domestic abuse and the perimenopause: a qualitative study with female survivors

open access articleDomestic abuse (DA) and perimenopause are each known to profoundly impact women’s health, yet their intersection remains largely unexplored. This study reveals how these experiences collide to create unique vulnerabilities and unexpected opportunities for transformation. This qualitative study explores how DA survivors experience perimenopause, examining the complexity and support needs that emerge when these experiences overlap. Methods Fifteen DA survivors participated in focus groups (and one individual interview) facilitated by a community leader of a DA survivors group exploring their perimenopause experiences. Data were analysed thematically using the one sheet of paper (OSAP) technique, with a DA survivor community leader (third author) involved throughout to support ethical engagement and participant well-being. Analysis revealed how trauma and hormonal changes interweave to shape help-seeking and survival. Results Three interconnected themes emerged: (1) symptom confusion and overlapping conditions - participants struggled to distinguish between trauma responses, hormonal changes, and pre-existing conditions; (2) weaponisation and empowerment - perpetrators exploited perimenopausal symptoms for coercive control, yet paradoxically, hormonal changes sometimes catalysed women’s decisions to leave; (3) barriers and facilitators - system failures drove survivors toward peer support networks that provided validation unavailable from professional services. Participants described heightened anxiety, mood changes, and sleep disturbances intensified by current or past abuse. The weaponisation of perimenopausal symptoms represents a previously unreported form of coercive control. Conversely, some participants described perimenopause as a moment of emotional clarity, contributing to decisions to leave relationships with an abusive partner. Conclusions The intersection of DA and perimenopause creates unique vulnerabilities that current UK support systems fail to address. Healthcare providers require training to recognise how trauma and hormonal symptoms can mask or mimic each other. The finding that perimenopause can serve as both a tool of abuse and a catalyst for liberation challenges deficit-focused narratives around both DA and perimenopause. Integrated, trauma-informed approaches are urgently needed across healthcare and support services

From food emergency to poverty prevention: The changing function of food banks in Leicester.

Policy BriefThis independent policy brief explores the evolving social function of food banks in Leicester. From our academic perspective, the intention is to support the city’s food bank network, the Leicester Food Partnership, the development of a Food Health Needs Assessment in the city, and the wider network of stakeholders constituting the Feeding Leicester Steering Group. While food banks continue to support people with the provision of emergency food parcels, they increasingly support the prevention of poverty in different ways. Poverty prevention refers to the wide range of functions that food banks are undertaking in relation to social welfare, including employability and financial management support. The increased need that the city has experienced recently, in particular after the Covid19 pandemic, led to the development of the Leicester Food Partnership (LFP), an informal arrangement between 22 food banks. This policy brief focuses on the LFP and its poverty prevention work in local communities

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

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

Rapport-building in suspect interviews: A survey of Lithuanian investigators

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.In criminal investigation interviewing settings, establishing rapport with interviewees has been successively found as paramount in successfully resolving a case. In the present exploratory study, we examined for the first time (to our knowledge) perceptions of Lithuanian police interviewers about what they actually do to obtain rapport when they conduct interviews with suspects. Sixty-one crime investigators took part in a survey where they were requested to answer both open-ended and close-ended questions relating to this subject. Respondents reported their employing a variety of rapport-building techniques. Lithuanian interviewers emphasized relationship-based rapport techniques (e.g., displaying empathy, friendliness, or humor) more so than procedural ones (e.g., when explaining to suspects their rights, or interview purpose) when describing what they did when interviewing suspects. Additionally, behaviors not found in the literature as ones relating to rapport, or those considered as counterproductive techniques, were occasionally reported as rapport-building approaches, especially when they dealt with uncooperative suspects. Also, it was found that respondents paid greater emphasis to the importance of rapport-building at the start of the interview but less so as interviews developed. Overall, Lithuanian criminal investigators appreciated the value of the rapport in interviews with suspects, nevertheless, challenges still remain

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

(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

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