Dynamical Theory Research Articles

Magnetic topological phases in the double exchange model with spin–orbit coupling

The double exchange model with the spin–orbit coupling is studied by the dynamical mean field theory. It reveals a competition between the magnetic, charge orderings and the non-trivial topology of the ground state. The spin exchange tends to maintain the magnetic ordering, and at the same time it tries to suppress the charge ordering. The spin–orbit coupling maintains nontrivial topology of the ground state, whereas the magnetic ordering tries to destroy it. As a result, a rich phase diagram is obtained. The competition leads to a half topological ground state, where spin-up electrons form a nontrivial topological state, while spin-down electrons are in the topological trivial insulating state.

DYNAMIC CAPABILITIES AND PERFORMANCE OF FAMILY BUSINESSES IN EMERGING ECONOMIES

Dynamic capabilities analyze the sources and methods of better performance and wealth creation and capture by firms operating in environments of rapid technological changes. Based on this, The objective of this research was to analyze the relationship between absorptive, adaptive, and innovation capabilities on financial and non-financial performance of family businesses was analyzed in the context of emerging economies, a relationship that has not been analyzed in this context. Through the application of structural equation modeling in a sample of 235 family businesses of agricultural supplies and machinery, located in the G46 and G47 categories of the International Standard Industrial Classification – ISIC, the results allowed us to identify that absorptive capability has a positive influence on financial performance, while innovation capability has a positive influence on no-financial performance. No evidence was found that other capabilities were related to the performance of the organizations analyzed. In addition, it was shown that the size of the companies does not generate any moderating effect in the relationship between these variables. This study contributes to dynamic capabilities theory by exploring how absorptive and innovative capabilities influence financial and non-financial performance in a specific and underexplored context: family businesses in emerging economies. Furthermore, the importance of developing and enhancing absorptive and innovation capabilities is highlighted. This could lead to the implementation of training programs, investment in R&D, and adoption of knowledge management practices.

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.

Responsible innovation and sustainable competitive advantage: is big data the missing link?

PurposeThis study aims to examine the influence of organizational flexibility (OF) and shared vision (SV) on sustainable competitive advantage (SCA) with the mediation role of responsible innovation (RI) in the manufacturing industry of a developing country. Furthermore, big data analytics capability (BDAC) serves as a moderator between RI and SCA.Design/methodology/approachThe study's hypotheses are investigated using the structural equation modeling (SEM) method. Through simple random sampling, information was gathered from 247 owners/managers of manufacturing SMEs.FindingsThe results elucidate that OF and SV significantly determine RI and SCA. Moreover, RI significantly mediates between SV, OF and SCA. Besides, RI significantly determines SCA. BDAC significantly leads to SCA. Finally, BDAC significantly moderates between RI and SCA.Research limitations/implicationsRI is crucial for manufacturing small and medium-sized enterprises (SMEs) to gain SCA and BDAC is important to address the changing demands of consumers for environment-friendly products. This study gives the public an overview of the different degrees to which SMEs are embracing RI and BDAC; with more environment-friendly initiatives, the natural environment will become more sustainable. Environmental sustainability will benefit each individual living in society.Originality/valueThis study adds value to the existing literature by focusing on predictors that affect SCA. Using dynamic capability theory, this initial study examines the influence of SV and OF on SCA and RI as mediators. Furthermore, BDAC is used as a moderating variable between RI and SCA. Managers, students and researchers can benefit from this study.

Development of a novel polydopamine-imprinted MOFs targeted for removal of refractory organic pollutants: Synergistic mechanisms of adsorption and degradation

In this study, a novel dopamine-imprinted MOFs (metal organic frameworks) was developed for the first time to achieve targeted degradation of refractory organic pollutants (ROPs) in advanced oxidation process. The targeted adsorption and degradation capacity of sulfamethoxazole (SMX) by molecularly imprinted catalyst (Fe-MOFs@MIP) was 3.17 times and 5.47 times that of Fe-MOFs, respectively. The results showed that the polydopamine (PDA) imprinted layer not only enhanced the targeted adsorption of SMX, but also improved the activity of the catalyst by promoting electron transfer. The physicochemical properties of Fe-MOFs@MIP were investigated. In situ Fourier transform infrared spectroscopy and molecular dynamics theory were used to investigate targeted adsorption mechanism. The active species were identified by quenching experiments and electron paramagnetic resonance (EPR). The reaction sites of SMX were predicted using density functional theory (DFT) and the possible degradation pathways of SMX were proposed. Finally, the targeted degradation mechanism of Fe-MOFs@MIP was elucidated. This study has a guiding significance for the deep removal of refractory organic pollutants in wastewater.

O01 Mapping the cellular and molecular dynamics of mouse and human hair follicles reveals mechanisms of hair growth

Abstract Introduction and aims Tissue homeostasis in continuously renewing organs, such as the skin, rely on orchestrated cellular and molecular dynamics. The hair follicle (HF) is characterized by distinct keratinocytes organized in transcriptionally and functionally distinct concentric HF layers. The anagen progenitor cells (also called germinative layer cells) are located juxtaposed to the dermal papilla and give rise to each of the differentiated layers. Despite this well-known high level of organization and cell-lineage separation, it is still unclear how the germinative layer cells coordinate hair growth and commit to specific lineages. Methods Here, we employed single-cell RNA sequencing, single-molecule mRNA fluorescence in situ hybridization and in vivo lineage tracing to show how the matrix progenitor cells commit and contribute to the HF lineages depending on their position. Furthermore, we developed a four-dimensional live imaging system of the human HF to construct a global map of single-cell dynamics. Results We show that the HF progenitors dynamically relocate in a conveyor-belt-like movement, switch their lineage fate and gradually restrict their potential. This spatiotemporal cell choreography is characterized by a gradual change of expression from Lgr5 in the lower proximal cup, through Dcn, Id3 and Msx1 in the central part, to distal Lef1 expression where the medulla lineage starts. Detailed in situ mRNA staining and mouse clonal lineage tracing show the transcriptional changes and gradual lineage restriction of matrix progenitors in the spatial context. Combining human HF imaging with fluid dynamics theories, we uncovered a spiral-like downward movement of outer root sheath cells, feeding the germinative layer stem cells, and revealed a pulling force induced by the outer root sheath cells as a critical driver for hair growth. Conclusions Taken together, our study uncovered how HF progenitor cells progress in a conveyor-belt-like manner through several transcriptional states and discovered new cellular and molecular mechanisms regulating hair formation.

Comparing social responses to Ebola and Covid-19 in Sierra Leone: an institutional analysis.

This paper compares community responses to Ebola and Covid-19 in two regions of southern and eastern Sierra Leone with reference to the theory of institutional dynamics proposed by the anthropologist Mary Douglas. Institutions, Douglas argued, are conveyed by styles of thought, shaped by the ways human communities, through everyday practices, reinforce systems of classification and denotation. Pandemic advice to 'follow the science' proved problematic, since there is no single institution of science, and institutions never stand alone but are bundled with other institutions, reflecting the manifold and intertwined practices of human social life. The paper explores some of the ways a traumatic epidemic of Ebola Virus Disease in Sierra Leone shaped a distinctive local response to this deadly infectious disease in the absence of an effective vaccine. This local approach emphasised social rules based on ideas about sequestration and testing. Communities then proposed to continue this rules-based approach to the pandemic of Covid-19 and showed little initial enthusiasm for vaccination. With Ebola, the adoption of rules resulted in dramatic drops in infection rates. But Covid-19 spreads in different ways, and good results from the application of social rules were much less apparent. The paper shows how communities began to grapple with this new situation. In some cases, vaccine hesitation was overcome by treating the requirement for vaccination as a new form of social discipline. More generally, it is concluded that epidemiologists need to pay specific attention to institutions and institutional dynamics in order to better understand and anticipate public reactions to new disease threats.

Federated learning in smart home: A dynamic contract-based incentive approach with task preferences

Federated Learning (FL) is a new type of distributed machine learning paradigm aimed at addressing data privacy and security issues in the Internet of Things (IoT) context. In the training process of federated learning model of smart home, data owners need to constantly update cached data, which lead to resources consumption and service latency. Meanwhile, the update cost of the data owners will change with the passage of time when the model owner requests data update. Therefore, this paper first puts forward an incentive scheme based on two-period dynamic contract theory under the information asymmetry. The scheme can balance the weighted preference of the model owner for age of information (AoI) and service latency, as well as encourage more data owners to participate in model training to increase the utility of model owner. Then we formally prove the feasibility of the proposed dynamic contract, which satisfies the constraints of individual rationality and intertemporal incentive compatibility. The experimental results on MNIST dataset show that the accuracy of the proposed dynamic contract is improved by at least 4% compared with the existing contracts. Additionally, compared with traditional contract and consistent pricing strategy, the model owner obtains more profit from the proposed contract.

Percolation phase transition, self-organization processes and thermal conductivity in PbSe1-xTex semiconductor solid solutions at small Te concentration

ABSTRACT The isotherms of the lattice thermal conductivity λ L for the PbSe1-xTex semiconductor solid solutions (х = 0–0.045) were obtained in the temperature range T = (200–600) K. The λ L(x) dependences exhibit a general trend to a decrease in λ L with increasing x, but the peaks are observed near x = 0.007, 0.015 and 0.0025 indicating the presence of phase transformations. We associated the first peak with a percolation-type phase transition to the impurity continuum and estimated the critical index for λ L within the framework of dynamic scaling theory. The second and third peaks were attributed to the self-organization processes in the PbSe1-xTex after the percolation threshold reaching. The effective cross-sections σ s for phonon scattering by impurity (Te) atoms were determined experimentally and calculated theoretically using the Klemens theory. The data obtained are another confirmation of our assumption about the presence of such percolation-type phase transition in any solid solution.

Evolutionary sensemaking: a managerial metacognitive dynamic capability to reduce information asymmetry

PurposeHow can managers reduce information asymmetry in dyadic manager-external stakeholder relationships in a complex and evolving environment? Addressing this question has significant implications for firm survival, growth, and competitive advantage.Design/methodology/approachWe have adopted a multiparadigm approach to theory building, known as metatriangulation. We integrate the dynamic capabilities, sensemaking, and evolutionary theory literatures to theorize how managers can relate to stakeholders in a complex and evolving environment.FindingsWe propose, via a conceptual framework and three propositions, “evolutionary sensemaking” as the managerial metacognitive dynamic capability that helps managers hone their understanding based on the evolutionary changes in the stakeholder’s interpretations of information quality preferences. The framework unfolds across three evolutionary stages: sensing preferences' variation of the stakeholder, seizing preferences, and transforming for complexity alignment and retention. The propositions focus on managing complexity in stakeholder information quality preference, employing cognitive capabilities to simplify, interpret, and align interpretations for effective information asymmetry reduction.Practical implicationsTo develop the metacognitive dynamic capability of evolutionary sensemaking, managers need to train for and foster the underlying complex cognitive capabilities by enhancing their (1) perception and attention skills, (2) problem-solving and reasoning skills, and (3) language, communication, and social cognition skills, focusing specifically on reducing the complexity embedded in stakeholder cognition and diverse stakeholder preferences for information quality. Contrary to the current advice to “keep things simple” and provide “more” information to the stakeholders for opportunism reduction, trust-building, and superior governance, our framework suggests that managers hone their cognitive capabilities by learning to deal with the underlying complexity.Originality/valueThe proposed framework and propositions address research gaps in reducing information asymmetry. It enriches the dynamic capabilities literature by recognizing complexity (as opposed to opportunism) as an alternative source of information asymmetry, which needs to be addressed in this stream of research. It extends the sensemaking literature by identifying the complexity sources – i.e. stakeholder preferences for diverse information quality attributes and the associated cognitive preference interpretation processes. The article enhances evolutionary theory by delving into microprocesses related to information asymmetry reduction, which the existing literature does not thoroughly investigate.

Study on dynamic response characteristics of the scanning angle in a liquid crystal cladding waveguide beam scanner

This paper studies the dynamic response characteristics of the scanning angle in a liquid crystal cladding waveguide beam scanner. Based on liquid crystal dynamic theory, finite element analysis and vectorial refraction law, a dynamic response calculation model of scanning angle is constructed. The simulation results show that the dynamic responses of the scanning angle during the electric field-on and field-off processes are asymmetric, and exhibit “S”-shape and “L”-shape changing trends, respectively. In addition, by comparing with the bulk phase modulation response process of traditional liquid crystal devices, the intrinsic physical reason for the rapid light regulation of the liquid crystal cladding waveguide beam scanner is clarified to be that the liquid crystal close to the core layer has a faster rotation speed during the electric field-off process. Moreover, the liquid crystal cladding waveguide beam scanner is experimentally tested, and the experiment results are in good agreement with theoretical simulations.

Investigating the Dynamic Behavior of Integer and Noninteger Order System of Predation with Holling’s Response

The paper’s primary objective is to examine the dynamic behavior of an integer and noninteger predator–prey system with a Holling type IV functional response in the Caputo sense. Our focus is on understanding how harvesting influences the stability, equilibria, bifurcations, and limit cycles within this system. We employ qualitative and quantitative analysis methods rooted in bifurcation theory, dynamical theory, and numerical simulation. We also delve into studying the boundedness of solutions and investigating the stability and existence of equilibrium points within the system. Leveraging Sotomayor’s theorem, we establish the presence of both the saddle-node and transcritical bifurcations. The analysis of the Hopf bifurcation is carried out using the normal form theorem. The model under consideration is extended to the fractional reaction–diffusion model which captures non-local and long-range effects more accurately than integer-order derivatives. This makes fractional reaction–diffusion systems suitable for modeling phenomena with anomalous diffusion or memory effects, improving the fidelity of simulations in turn. An adaptable numerical technique for solving this class of differential equations is also suggested. Through simulation results, we observe that one of the Lyapunov exponents has a negative value, indicating the potential for the emergence of a stable-limit cycle via bifurcation as well as chaotic and complex spatiotemporal distributions. We supplement our analytical investigations with numerical simulations to provide a comprehensive understanding of the system’s behavior. It was discovered that both the prey and predator populations will continue to coexist and be permanent, regardless of the choice of fractional parameter.

Digital knowledge management effect on enterprise technological innovation: an empirical study from China’s manufacturing industry

ABSTRACT With digital technology permeating across the layers of businesses, digital knowledge management has emerged, transforming the means through which companies acquire, integrate, share, and innovatively apply knowledge. However, the specific impact of digital knowledge management on corporate technological innovation remains unclear. Based on dynamic capabilities theory, this study delves into how digital knowledge management influences corporate technological innovation. Analyzing data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2007 to 2021, this study confirms that digital knowledge management significantly and positively affects corporate technological innovation by enhancing a company's absorptive capacity, adaptability, and innovation ability. Based on the findings, this study suggests that companies should leverage digital knowledge management to acquire and integrate external innovative resources, seek cooperation opportunities with external stakeholders, and jointly build a mutually beneficial innovation knowledge network, thereby boosting technological innovation capabilities. Additionally, companies should encourage employees to learn digital technologies and create a culture conducive to learning and innovation.

Eco-innovation and SMEs’ sustainable performance: a meta-analysis

PurposeThe study aims to build upon the Resource-based view of the firm (RBV) and Dynamic Capability Theory (DCT) to perform a meta-analysis on the eco-innovation/SMEs’ sustainable performance relationship.Design/methodology/approachEmploying a psychometric meta-analytic approach with a random-effects model, the study examines a sample of 134,841 SMEs covering 99 studies and 233 study effects. Subgroup and meta-regression analysis were used to test the study`s hypotheses in Comprehensive Meta-Analysis (CMA) statistical software.FindingsResults unveil that the average impact of eco-innovation on SMEs` sustainable performance is positively significant but moderate. Moreover, it was found that eco-process, eco-product, eco-organizational, and eco-marketing innovations positively influence SMEs’ sustainable performance, but the impact of eco-organizational innovation is the strongest. Findings further reveal that eco-innovation positively influences economic, social, and environmental performance, but its effect on social performance is the largest. Moreover, our findings reveal that contextual factors, including industry type, culture, industry intensity, global sustainable competitive index, and human development index, moderate the eco-innovation/SMEs’ sustainable performance relationship. Lastly, methodological factors, namely sampling technique, study type, and publication status, account for study-study variance.Practical implicationsOur findings imply that investing in eco-innovation is worthwhile for SMEs. Therefore, CEOs/managers of SMEs must adopt eco-innovation initiatives by establishing a sustainability vision, developing employee environmental development and training, building a stakeholder management system, and promoting employee engagement in sustainability activities.Originality/valueThe study develops a holistic conceptual framework to consolidate the distinct types of eco-innovation and their association with the sustainable performance of SMEs for the first time in this research stream, thereby resolving the anecdotal results and synthesizing the fragmented literature across culture, discipline, and contexts.

Nonlinear evolution characteristics and seepage mechanical model of fluids in broken rock mass based on the bifurcation theory

With the deep extension of coal mining in China, fault water inrush has become one of the major disasters threatening the safety production of coal mine. Based on the control equations of steady state and non-Darcy seepage in fractured rock mass, the multi-parameter nonlinear dynamic seepage equations of fractured rock mass are established in this paper. Based on the nonlinear dynamics theory, the function of the state variable in the system is derived, and the influence of the gradual change of non-Darcy flow factors on the structural stability of seepage system is studied. The research achievements show that there are three branches in the equilibrium state of the seepage system. Specifically, the stability of the equilibrium state changes abruptly near the limit parameter. The seepage dynamic system of fractured rock mass has the delayed bifurcation, and the coal mine disaster such as fault water inrush occurs easily at the bifurcation point. The research results are of great significance to enrich the theory of fault water inrush in coal mine, and to reveal the disastrous mechanism of fault water inrush and guide its prevention and control technology in coal mine, which can provide the theoretical reference for predicting the water seepage stability in fractured rock mass.

Load characteristics of screen panels in vibrating flip-flow screens

A vibrating flip-flow screen (VFFS) is an effective solution for screening viscous and fine-grained materials. As one of the critical components, the load characteristics of the polyurethane screen panel primarily affect the fatigue life. However, more research is needed to understand the load characteristics of the screen panel. In this article, the discrete screen panel dynamic model is proposed to research the load characteristics of the screen panel based on the catenary model. The dynamic response parameters of the screen panel are obtained by the catenary model, and the finite element model of the screen panel is established to extract the dynamic characteristic parameters of the screen panel. The accuracy of the catenary model and the finite element model is verified by the experiment, and then the rationality of the dynamic theory is verified by the simulation. Moreover, the effects of hardness, amplitude, frequency, and angle on the tension and vibration strength of the screen panel are investigated. The results show that hardness, amplitude, frequency, and angle significantly affect the tension and vibration strength of the screen panel. The sensitivity of each factor on tension from high to low: amplitude > hardness > frequency > angle. Adjusting the value of each factor within a certain range is beneficial to increasing the vibration strength; meanwhile, it can reduce the tension of the screen panel. This research helps improve the service life and provides a theoretical basis for designing and optimizing the screen panel.

Freeze-out of perturbation growth for shocked heavy fluid layers by eliminating reverberating waves

The shock wave accelerating a heavy fluid layer can induce reverberating waves that continuously interact with the first and second interfaces. In order to manipulate the perturbation growths at fluid-layer interfaces, we present a theoretical framework to eliminate the reverberating waves. A model is established to predict the individual freeze-out (i.e. stagnation of perturbation growth) for the first and second interfaces under specific flow conditions determined based on the shock dynamics theory. The theoretical model quantifies the controllable parameters required for freeze-out, including the initial amplitudes of the first and second interfaces, the interface coupling strength and the maximum initial layer thickness preventing the second interface's phase reversal. The effectiveness of the model in predicting individual freeze-out for the first and second interfaces is validated numerically over a wide range of initial conditions. The upper and lower limits of initial amplitudes for the freeze-out of the whole fluid-layer width growth are further predicted. Within this amplitude range, a slightly higher initial amplitude for the second interface is specified, effectively arresting the growth of the entire fluid-layer width before the phase reversal of the second interface.

Unpacking the impact of entrepreneurial learning on business model innovation in internet startups: Mediating roles of digital capabilities

While researchers recognize the central role of business model innovation (BMI) in the success of digital startups, empirical research systematically exploring the pathways of BMI in the digital era for such startups remains scarce. This study employs the dynamic capability theory to construct a theoretical framework termed the “learning capability model” to examine the influence of entrepreneurial learning on BMI. We collected questionnaires from 386 Internet startups and utilized hierarchical regression analysis to explore the impact of digital capabilities on these startups. We first identified two different types of entrepreneurial learning: exploratory learning and exploitative learning. Subsequently, we tested for their effects on BMI. The empirical results indicate that both exploratory and exploitative learning significantly impact BMI. However, exploitative learning exerts a greater influence than exploratory learning. These tests revealed that digital sensing capabilities, digital operational capabilities, and digital resource collaboration capabilities have a partial mediating effect on the relationship between entrepreneurial learning and BMI. Among these, digital operational capabilities exert the most significant mediating effect, while digital sensing capabilities exhibit weaker mediating effects. This study contributes to our understanding of digital capabilities and provides practical insights for Internet startups.

Influence of operational excellence strategy on organisations’ competitiveness among tier III category of Commercial Banks in Kenya

Tier 3 Kenyan commercial banks face significant challenges, including intense competition, financial distress, and declining profits despite consistent loan growth. This study investigates the impact of operational excellence strategies on the competitiveness of these banks using a pragmatic research approach that combines qualitative and quantitative methods. The study is grounded in the dynamic capabilities theory and collected data from 288 respondents across various management roles within the banks through semi-structured questionnaires. The data were analyzed using thematic analysis for qualitative data and SPSS for quantitative data, including descriptive statistics, regression analysis, and ANOVA. The regression model revealed a moderate positive relationship between operational excellence and organizational competitiveness, with a correlation coefficient (R) of 0.685. The coefficient of determination (R Square) was 0.469, indicating that operational excellence accounts for 46.9% of the variability in competitiveness. The coefficient for Operational Excellence is 1.260 (standard error = 0.087, beta = 0.685), with a t-value of 14.493 and a significance level of 0.000. The constant coefficient is -1.565 (standard error = 0.353, t-value = -4.437). This study underscores the importance of strategic investments in operational excellence practices to enhance organizational efficiency and sustainable competitive advantages. However, the study's scope is limited to tier III commercial banks in Kenya, and future research should explore additional factors influencing competitiveness and include a broader sample for improved generalizability.

Analyzing physical activity impact based on ubiquitous nonlinear dynamics and electroencephalography data.

Understanding complex systems is made easier with the tools provided by the theory of nonlinear dynamic systems. It provides novel ideas, algorithms, and techniques for signal processing, analysis, and classification. Presently, these ideas are being applied to the investigation of how physiological signals evolve. The study applies nonlinear dynamics theory to electroencephalogram (EEG) signals to better comprehend the range of alcoholic mental states. One of the main contributions of this paper is an algorithm for automatically distinguishing between sober and drunken EEG signals based on their salient features. The study utilized various entropy-based features, including ApEn, SampEn, Shannon and Renyi entropies, PE, TS, FE, WE, and KSE, to extract information from EEG signals. To identify the most relevant features, the study employed ranking methods like T-test, Wilcoxon, and Bhattacharyya, and trained SVM classifiers with the selected features. The Bhattacharyya ranking method was found to be the most effective in achieving high classification accuracy, sensitivity, and specificity. Classification accuracy of 95.89%, the sensitivity of 94.43%, and specificity of 96.67% are achieved by the SVM classifier with radial basis function (RBF) for polynomial Kernel using the Bhattacharyya ranking method. From the result, it is clear that the model serves as a cost-effective and accurate decision-support tool for doctors in diagnosing alcoholism and for rehabilitation centres to monitor the effectiveness of interventions aimed at mitigating or reversing brain damage caused by alcoholism.