Methodological Insights Research Articles

ASSESSING THE SYNERGETIC EFFECT OF SELECTING THE OPTIMAL STRUCTURE OF A LOGISTICS CHAIN

This study tackles a critical challenge in logistics optimization: assessing the economic efficiency not only of individual entities within a logistics chain, but also the synergistic benefits that arise from their collaboration. We achieve this by proposing a methodology that evaluates the economic efficiency of interactions between participants in a logistics chain. This methodology goes beyond individual efficiency and delves into how the overall economic benefit is distributed among key stakeholders. These stakeholders include freight owners, who initiate the delivery process, forwarders who manage and optimize deliveries, carriers who physically transport goods, and freight terminals that facilitate cargo handling and storage. To ensure the methodology’s relevance to contemporary practices, we begin with a comprehensive review of recent advancements in delivery chain optimization research. We propose to measure the synergetic effect by considering delivery demand parameters, such as the weight of the consignment and the distance it needs to travel. To validate our methodology and gain practical insights, we conducted a series of experimental studies specifically tailored to the Kazakhstani transportation market. By analysing the share of the synergistic effect under varying delivery demand parameters, we were able to identify trends and patterns.

An artificial bee colony optimization algorithms for solving fuzzy capacitated logistic distribution center problem

This paper presents a methodological approach to solving the fuzzy capacitated logistic distribution center problem, with a focus on the optimal selection of distribution centers to meet the demands of multiple plants. The distribution centers are characterized by fixed costs and capacities, while plant demands are modeled using fuzzy triangular membership functions. The problem is mathematically formulated by converting fuzzy demands into crisp values, providing a structured framework for addressing uncertainty in logistic planning. To support future research and facilitate comparative analysis, 20 benchmark problems were generated, filling a gap in the existing literature. Three distinct artificial bee colony algorithm variants were hybridized with a heuristic: one using the best solution per iteration, another incorporating chaotic mapping and adaptive procedures, and the third employing convergence and diversity archives. An experimental design based on Taguchi's orthogonal arrays was employed for optimizing the algorithm parameters, ensuring systematic exploration of the solution space. The developed methods offer a comprehensive toolkit for addressing complex, uncertain demands in logistic distribution, with code provided for reproducibility.Key contributions include:•Development of a fuzzy model for the selection of distribution centers with fixed costs and capacities under uncertain plant demands.•Generation of 20 benchmark problems to advance research in the fuzzy capacitated logistic distribution center problem domain.•Integration of a heuristic approach with three distinct ABC algorithm variants, each contributing unique methodological insights.

Graph pooling in graph neural networks: methods and their applications in omics studies

Graph neural networks (GNNs) process the graph-structured data using neural networks and have proven successful in various graph processing tasks. Currently, graph pooling operators have emerged as crucial components that bridge the gap between node representation learning and diverse graph-level tasks by transforming node representations into graph representations. Given the rapid growth and widespread adoption of graph pooling, this review aims to summarize the existing graph pooling operators for GNNs and their representative applications in omics. Specifically, we first present a comprehensive taxonomy of existing graph pooling algorithms, expanding the categorization for both global and hierarchical pooling operators, and for the first time reviewing the inverse operation of graph pooling, named unpooling. Next, we describe the general evaluation framework for graph pooling operators, encompassing three fundamental aspects: experimental setup, ablation analysis, and model interpretation. We also discuss open issues that significantly influence the design of graph pooling operators, including complexity, connectivity, adaptability, additional loss, and attention mechanisms. Finally, we summarize bioinformatics applications of graph pooling operators in omics, including graphs of gene interaction, medical images, and protein structures for drug discovery and disease diagnosis. Furthermore, we showcase the impact of graph pooling operators on research in specific real-world domains, with a focus on prediction performance and model interpretability. This review provides methodological insights in machine learning based graph modeling and related omics research, as well as an ongoing resource by gathering related papers and code in a dedicated GitHub repository (https://github.com/Hou-WJ/Graph-Pooling-Operators-and-Bioinformatics-Applications).

CRATSM: An Effective Hybridization of Deep Neural Models for Customer Retention Prediction in the Telecom Industry

In the dynamic field of Customer Retention Prediction (CRP), strategic marketing and promotion efforts targeting specific customers are crucial. Understanding customer behavior and identifying churn indicators are vital for devising effective retention strategies. However, identifying customers likely to terminate services presents a challenge, leading to data imbalance issues. Existing CRP studies using Machine Learning (ML) techniques and data imbalance methods face problems such as overfitting and computational complexity. Similarly, recent CRP studies employing Deep Learning (DL) approaches rely on data sampling techniques, which can result in overfitting and a lack of cost sensitivity. Additionally, DL approaches struggle with slow convergence and get stuck in local minima. This paper introduces an effective hybrid of Deep Learning (DL) classifiers focusing on cost-metric integration to address data imbalance issues and period-shift Cosine Annealing Learning Rate (ps-CALR) to accelerate model training, ultimately enhancing performance. Three Telecom datasets, namely IBM, Iranian, and Orange, were used to assess the model performance. Empirical findings show that the hybrid DL classifiers significantly improved CRP over conventional ML. This paper contributes methodological advancements and practical insights for effective customer retention in the telecom industry.

Refining Predictive Models for Urolithiasis: Methodological Insights and Clinical Implications.

We have reviewed the article "Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights" by Chmiel et al. with keen interest. The authors have made significant strides in leveraging machine learning to predict urinary stone composition, a crucial factor in the management and treatment of urolithiasis. While the study presents innovative methodologies and insightful findings, there are several areas where the approach and interpretation could be refined to enhance the robustness and applicability of the results.

Core outcome set for intervention research on snakebite envenomation in South Asia

BackgroundThe 2019 WHO strategy to reduce snakebite burden emphasises the need for fostering research on snakebite treatments. A core outcome set (COS) is a consensus minimal list of outcomes that...

Pragmatism and Conflicts at Work: Theoretical and Methodological Insights from William James

Pragmatism and Conflicts at Work: Theoretical and Methodological Insights from William James

Configurations of democracy—empirical and methodological insights from a comparison of Singapore, Ghana, and Ireland

This article presents and proofs an alternative concept of democracy that seeks to overcome the limitations of rigid universalist, liberal-proceduralist conceptions by emphasizing the fundamental principles of democracy rather than combining them with culturally individualistic features. The approach presented here focuses on the fundamental principles of democracy. Democratic configurations assume that citizens’ political self-efficacy of the people is a potential basic principle behind any institutionalization of democratic order. Therefore, this article refers on a discussion of the theoretical implementation of self-efficacy in the three models of democracy: liberalism, republicanism and communitarianism. Ultimately, every political system must be studied by whether the established institutions serve this basic principle. The article illustrates the proposed approach through case studies of Singapore, Ghana, and Ireland. The empirical examples show how different institutional settings and their adjustments strengthen and hinder political self-efficacy. Therefore, this new bottom-up-approach of studying configurations of democracy may help to get better insights on the democraticness of political systems and other institutional settings.

The Oromo Liberation Front and its Long Journey: Achievements, Challenges, and Resilience

This article identifies and examines several interrelated issues concerning the Oromo Liberation Front (OLF), which has struggled to liberate Oromo society from Abyssinian/Ethiopian settler colonialism and its authoritarian-terrorist state for almost a half-century. These issues include the formation of the front, its objectives, accomplishments, and obstacles, the emergence of the Abiy Ahmed government, and the consolidation and expansion of the Oromo Liberation Army (OLA). The piece starts its analysis by providing methodological and theoretical insights.

Estimating genus-specific effects of non-native honey bees and urbanization on wild bee communities: A case study in Maryland, United States

Non-native species have the potential to detrimentally affect native species through resource competition, disease transmission, and other forms of antagonism. The western honey bee (Apis mellifera) is one such species that has been widely introduced beyond its native range for hundreds of years. There are strong concerns in the United States, and other countries, about the strain that high-density, managed honey bee populations could pose to already imperiled wild bee communities. While there is some experimental evidence of honey bees competing with wild bees for resources, few studies have connected landscape-scale honey bee apiary density with down-stream consequences for wild bee communities. Here, using a dataset from Maryland, US and joint species distribution models, we provide the largest scale, most phylogenetically resolved assessment of non-native honey bee density effects on wild bee abundance to date. As beekeeping in Maryland primarily consists of urban beekeeping, we also assessed the relative impact of developed land on wild bee communities. Six of the 33 wild bee genera we assessed showed a high probability (> 90 %) of a negative association with apiary density and/or developed land. These bees were primarily late-season, specialist genera (several long-horned genera represented) or small, ground nesting, season-long foragers (including several sweat bee genera). Conversely, developed land was associated with an increase in relative abundance for some genera including invasive Anthidium and other urban garden-associated genera. We discuss several avenues to ameliorate potentially detrimental effects of beekeeping and urbanization on the most imperiled wild bee groups. We additionally offer methodological insights based on sampling efficiency of different methods (hand netting, pan trapping, vane trapping), highlighting large variation in effect sizes across genera. The magnitude of sampling effect was very high, relative to the observed ecological effects, demonstrating the importance of integrated sampling, particularly for multi-species or community level assessments.

Tate Liverpool’s Democracies: curatorial methodologies for exploring democracy

ABSTRACT This article reconstructs and juxtaposes two radically different methodologies for approaching the topic of democracy – one exemplified by major UK art gallery Tate Liverpool’s exhibition called Democracies (2020–2023), the second a set of approaches characteristic of work in the academic field of contemporary democratic theory. By comparing a range of key features of the two methodologies, the article draws out ways in which methodological insights from curation-as-research may usefully extend, deepen or challenge those characteristic of academic democratic theory.

가상가치평가법(CVM)을 활용한 청와대(靑瓦臺) 이용가치 추정

Purpose: This study employs a 2-stage dichotomous choice contingent valuation method (CVM), to evaluate the use value of Cheongwadae (the Blue House)--the former presidential office and residence of South Korea--which opened for tourist visits in May 2022. Methods: To minimize the hypothetical bias, hypothetical and actual scenarios (cash/card payment) were jointly used. Survey data of 400 Cheongwadae visitors were collected, and the data were analyzed using willingness to pay (WTP) estimation and logistic regression analysis. Results: Logistic regression analysis revealed that bid amount, landmark attributes (form, symbolism), experience factors (esthetic, escape), gender, age, marital status, visitor type, and political orientation significantly influenced respondents’ willingness to pay (WTP) for entry fees. The estimated per-person use value, based on the truncated mean WTP, was KRW 6,002 in the hypothetical scenario and KRW 2,347 in the actual scenario. This difference suggests that using an actual scenario and a truncated mean is more appropriate for minimizing hypothetical bias and estimating a more accurate value. Considering the total number of visitors in the past 12 months (3,381,664, May 2023), the total use value of Cheongwadae was calculated as KRW 7937.37 million. Conclusion: This study’s findings highlight Cheongwadae’s significant economic and cultural value as a landmark. The results can assist public awareness campaigns regarding Chengwadae’s socio-economic importance and guide government decisions for its preservation and development as a tourism attraction. Furthermore, the methodological insights on minimizing hypothetical bias in CVM studies can be applied to the future valuation of cultural heritage sites.

Drought risk management in agriculture: A copula perspective on crop diversification

AbstractDrought events are a major cause of large crop yield losses with implications for food security and farmers’ incomes. Growing multiple crops simultaneously during a cropping season is a well‐known on‐farm risk management strategy to cope with these drought risks. However, the effectiveness of this crop diversification under different severity levels of drought and how this effectiveness is influenced by the crop composition is unclear. This article provides new methodological and empirical insights to assess the effectiveness of such diversification, in particular to cope with extreme drought. We apply and evaluate nested Archimedean copulas and elliptical copulas to assess simultaneous farm‐level yield losses of different cash crops in German agriculture (winter wheat, winter barley, winter rapeseed, sugar beet, and grain maize) under different drought severity levels (N = 249,756; regionally pooled farm‐level crop‐yield pairs, 1995–2019). We show that on‐farm crop diversification contributes to cope with drought risks, but its effectiveness varies considerably across regions, crop pairs, and drought severity. Our results underline that cropping system diversification alone is often not sufficient to cope with drought risks, but that the right crop combinations are needed. For example, during a severe drought (one in 20 years event), 26.4% of farmers in eastern Germany suffered simultaneous yield losses of at least 20% in winter wheat and winter barley, while 19.1% of farmers in eastern Germany suffered simultaneous yield losses of at least 20% in winter wheat and sugar beet. Farmers should therefore be encouraged to grow crops with more diverse phenological requirements throughout the year.

Methodological Insights into Implementing cellular automata models for simulating seagrass dynamics: Responses to global change effects

This study introduces an innovative methodology employing Cellular Automata (CA) models to simulate seagrass dynamics in response to global environmental changes. The primary objective is to outline a procedural framework for constructing and deploying CA models applied to seagrass ecosystems, and potentially to other marine or terrestrial environments. The methodology encompasses various components, including conceptualization, workflow delineation, model parameterization, and execution steps. By utilizing Mediterranean and Zanzibari (East Africa) seagrass ecosystems as case studies, we demonstrate the versatility and applicability of the proposed approach across diverse geographical regions, species composition and model components. Through these case studies, we demonstrated how CA models can effectively capture the dynamics of seagrass communities subjected to climate change, invasive species, and nutrient regimes. Despite its strengths, the proposed CA model has limitations, including parameterization complexity and uncertainties related to species-specific environmental thresholds, growth rates and species interactions, alongside the difficulty of validating our models with real-world scenarios. Addressing these limitations in future studies will enhance the model's accuracy and applicability. This study serves as a foundation for future research in other regions and ecosystems, facilitating a better understanding of the complex interactions driving ecosystem dynamics.•This study introduces a methodology using Cellular Automata (CA) models to simulate seagrass dynamics detailing conceptualization, workflow, parameterization, and execution.•Case studies in Mediterranean and East Africa ecosystems demonstrate the versatility of CA models in capturing the impacts of climate change, invasive species, and nutrient regimes.•Despite strengths, the CA model has limitations and uncertainties like parameterization complexity and model validations suggesting future research to enhance accuracy and applicability.

Assessing the environmental justice implications of decarbonizing the US electric grid: estimating changes in asthma exacerbation by race and income

Abstract This paper examines how air quality improvements due to the 100% decarbonization of the US power sector in 2040 can reduce asthma exacerbation among children disaggregated by poverty status, race, and geography. Using spatial datasets that differentiate asthma prevalence by income, race, and state, we find that children living in households with income below the poverty line receive a disproportionate share of the benefits. To obtain these results, we employ several different federally administered datasets: American Community Survey, Behavioral Risk Factor Surveillance System (BRFSS), and Poverty Thresholds as provided by the US Census. We find that Black children and poor children, on average, have higher reductions in exposure to PM2.5. Nationally, close to 235 372 asthma exacerbation cases will be averted in 2040 under the decarbonization policy compared with Business-as-Usual. States with significant gains in asthma cases averted per 100 000 are Indiana, Kentucky, Ohio, Missouri, Pennsylvania, Texas, and Wisconsin. Furthermore, since the asthma portion of the BRFSS is not conducted in South Carolina, Colorado, Arkansas, and South Dakota, these states were omitted from the analysis. Across all states with significant gains, children living below the poverty line have larger health benefits than children above the poverty line. Households with child poverty have 50% larger reductions in asthma exacerbations than households without childhood poverty. Black children below the poverty line experience 33% higher health gains compared to Black children per 100 000 above the poverty line, 50% higher health gains compared to White children below the poverty line, and 159% higher than White children above the poverty line. We also provide general methodological insights for quantifying the environmental justice impacts of regulatory policies. We demonstrate why using race and poverty status-based prevalence rates is critical for understanding the distribution of health improvements and evaluating whether policies contribute to environmental justice goals.

The spatiality of a military base as urban geographical entity: the case of ‘The Pikkud’ in Beersheba, Israel

Urban studies have increasingly engaged in understanding the role of the military in co-producing the city. Yet, the spatial role of an individual military base within a particular city has largely been overlooked. This deficiency is particularly acute as, due to contradictory governance regimes, a military base constitutes a contradictory non-democratic interface with a liberal urban civil space. This deficiency is due primarily to failure to view a military base as a concrete ‘geographical entity’ with a geographical agency. The Lefebvrian spatial trialectic (perceived conceived and lived spaces) is employed here as a framework for analyzing the relationships between the base and the city. We bind it theoretically and analytically with the concept of a geographical entity carrying a geographical agency. Based primarily on a qualitative methodology, we studied the spatiality of an urban military base in Beersheba, Israel, analyzing the three facets of spaces. The analysis reveals that neoliberal concepts held by urban planning professionals regarding urban space as conditioned by the presence of the military base (conceived space) are not necessarily congruent with those of the public which routinely lives and experiences (lived space) the concrete materiality of the base (perceived space). Thus, as a geographical entity within the city, a military base exists apart yet concurrently there are overlapping relations and entanglements with community life, suggesting a spatiality that extends beyond its physical territory. With these conceptual and methodological approaches and insights this study adds to the recently growing field of urban military geographies which hitherto overlooked these spatial perspectives of the military.

Molecular Interactions in Atomically Precise Metal Nanoclusters.

For nanochemistry, precise manipulation of nanoscale structures and the accompanying chemical properties at atomic precision is one of the greatest challenges today. The scientific community strives to develop and design customized nanomaterials, while molecular interactions often serve as key tools or probes for this atomically precise undertaking. In this Perspective, metal nanoclusters, especially gold nanoclusters, serve as a good platform for understanding such nanoscale interactions. These nanoclusters often have a core size of about 2 nm, a defined number of core metal atoms, and protecting ligands with known crystal structure. The atomically precise structure of metal nanoclusters allows us to discuss how the molecular interactions facilitate the systematic modification and functionalization of nanoclusters from their inner core, through the ligand shell, to the external assembly. Interestingly, the atomic packing structure of the nanocluster core can be affected by forces on the surface. After discussing the core structure, we examine various atomic-level strategies to enhance their photoluminescent quantum yield and improve nanoclusters' catalytic performance. Beyond the single cluster level, various attractive or repulsive molecular interactions have been employed to engineer the self-assembly behavior and thus packing morphology of metal nanoclusters. The methodological and fundamental insights systemized in this review should be useful for customizing the cluster structure and assembly patterns at the atomic level.

Conspiratorial narratives as cultural repertoires and methodological tools.

This article builds on data and field work notes from two ethnographic studies conducted in two cities: Istanbul and Trabzon, Turkey. It examines the socio-political dynamics behind the prevalence and impact of conspiratorial narratives. We explore the emergence, circulation, and effects of these narratives and how they shape political orientations and mobilisation. We raise methodological questions about these narratives and propose researchers closely scrutinise them rather than dismissing them as illogical or incoherent. Our research reveals three novel relational and methodological insights derived from conspiratorial narratives. First, these narratives serve as sense-making tools during times of uncertainty. They provide accessible explanations for abrupt changes, and they rely and draw upon 'cultural repertoires'. Second, by challenging the mainstream narratives, they shape subjectivities; empowering narrators to act as agents. Third, how conspiratorial narratives circulate has implications for the dynamics of state-public relationships, often following the neoliberal logic, they portray political leaders as central figures in navigating complex decision-making processes. Our case studies demonstrate that actors, even in less powerful positions, may not necessarily antagonise the state. We underscore the methodological significance of these narratives for researchers, to examine actors' agency, group dynamics, and responses to everyday injustices.

Mito-kaede photoactivation and chase experiment for mitophagy: optimizing flux measurement via fluid exchange system.

Modulating autophagy and mitophagy, vital cellular quality control systems, offer therapeutic potential for critical illnesses. However, limited drug screening options hinder progress. We present a novel assay using the photoswitchable fluorescent reporter, mito-Kaede, to quantify mitophagy flux. Mito-Kaede's superior UV-induced photoconversion and brightness post-conversion make it ideal for prolonged mitochondrial dynamics tracking. Its specificity in responding to mitophagy, confirmed by parkin-knockout cells, adds value. When coupled with a custom fluid exchange system, enabling efficient medium changes, precise mitophagy observations become feasible. This mitophagy assay, alongside our methodological insights, can decipher mitophagy's role in pathology and supports drug screening efforts.

Utilizing sociocultural identity to cope with poverty-related stress: methodological insights from developing a coping intervention

ABSTRACT Cultural identity affirmation, pride, and belongingness are known to protect youth from negative outcomes in the face of uncontrollable stresses such as discrimination, economic hardship, and community violence. Scholars have argued that racial, cultural, and other social identities serve as important coping resources, encouraging active coping and the use of social support and mitigating the impact of stress for marginalized youth. The Building a Strong Identity and Coping Skills (BaSICS) program was designed to improve mental health by targeting the skills, assets, and resources known to protect against developing emotional and behavioral symptoms and disorders. In the BaSICS intervention, youth explore their cultural and social identities to strengthen their understanding of, and connection to, positively supportive social groups via exploration of family and community cultural heritage. Youth are then encouraged to engage their collaborative coping skills founded in their sociocultural identities, working together on a project of collective community action. This methodological paper will describe the theory and development of the BaSICS identity development and collective coping module.