Information Flow Research Articles

META-MODULE. Contemporary modularity as generative tool for Smart Architecture.

Abstract The complexity of the contemporary era challenges traditional methods and processes in architecture, exacerbated by diverse specializations, global production, and recent cultural, social, and technological shifts. Consequently, contemporary architecture responds to societal demands through innovative methods and tools. To maintain coherence in design objectives, explicit information on addressing contemporary concerns is essential. Systematizing informational flows becomes the language of Smart Architecture, utilizing informative patterns and design variations to address these challenges effectively. Starting with an analysis of the inherent characteristics of the modular system, considered both as a regulator of complexity and in its evolution within the architectural panorama influenced by significant socio-cultural and ideological transformations, this contribution introduces the concept of the meta-module. In the era of smart architecture, the module tends to evolve into a meta-module, serving as a generative tool for Smart Architecture. The meta-module integrates interconnected information to organize contemporary complexity, employing innovative design components across multiple scales to address various concerns. The originality of this contribution lies in a process of abstraction that transforms the concept of the module into that of a ‘meta-module’, as a multi-level support matrix for design, compatible with cutting-edge computer tools, ensuring coherence between design considerations and corresponding construction choices.

GLIC: Underwater target detection based on global–local information coupling and multi-scale feature fusion

With the rapid development of object detection technology, underwater object detection has attracted widespread attention. Most of the existing underwater target detection methods are built based on convolutional neural networks (CNNs), which still have some limitations in the utilization of global information and cannot fully capture the key information in the images. To overcome the challenge of insufficient global–local feature extraction, an underwater target detector (namely GLIC) based on global–local information coupling and multi-scale feature fusion is proposed in this paper. Our GLIC consists of three main components: spatial pyramid pooling, global–local information coupling, and multi-scale feature fusion. Firstly, we embed spatial pyramid pooling, which improves the robustness of the model while retaining more spatial information. Secondly, we design the feature pyramid network with global–local information coupling. The global context of the transformer branch and the local features of the CNN branch interact with each other to enhance the feature representation. Finally, we construct a Multi-scale Feature Fusion (MFF) module that utilizes balanced semantic features integrated at the same depth for multi-scale feature fusion. In this way, each resolution in the pyramid receives equal information from others, thus balancing the information flow and making the features more discriminative. As demonstrated in comprehensive experiments, our GLIC, respectively, achieves 88.46%, 87.51%, and 74.94% mAP on the URPC2019, URPC2020, and UDD datasets.

Sistem Pemasaran Beras Bujang Marantau Di Kecamatan Sungai Tarab, Kabupaten Tanah Datar, Provinsi Sumatera Barat

The “Bujang Marantau” rice variety is local rice from Tanah Datar Regency which was registered as a national superior rice variety by BPTP West Sumatra in 2015 and grows well in Tanah Datar Regency. This increase in “Bujang Marantau” rice production must be balanced with a good marketing system so that farmers and institutions marketing can receive more favorable prices. The study is aimed to analyze the channels, functions and operational efficiency of rice marketing (marketing margin, farmer’s share, and profit to cost ratio) in Sungai Tarab District. This research involved 45 farmers as samples obtained through a simple random sampling method. Marketing institution respondents were obtained using the snowball method based on the flow of information from farmers and 15 marketing institution respondents were obtained. Qualitative data is used to analyze marketing channels and institutions. Quantitative data is used to analyze marketing margin, farmer’s share, profit to cost ratio and marketing efficiency index. The results showed that there are 3 marketing channels and 3 types of marketing institutions involved. The results of the marketing operational efficiency analysis show that the relatively efficient marketing channel is channel III because it has the lowest marketing margin value, the highest farmer’s share, and the shortest marketing channel compared to other channels. Marketing institutions should further optimize the marketing functions of each institution, such as the transportation function, so that transportation costs can be lower and minimize marketing costs more efficiently.

Fake News Detection with Machine Learning Algorithms

Nowadays, with the advancement of technology, the use of news sources has also undergone a great evolution. News sources have constantly evolved from past to present, ranging from magazines to radios, from newspapers to televisions. The fact that it has become so easy to access news has caused society to pay more attention to fake news. Fake news has the ability to influence society through news sources such as social media, which can reach wider audiences with the development of technology. The difficulties of users in accessing accurate and reliable sources in this information flow that shapes their daily lives increases the potential for the spread of fake news, and it becomes increasingly difficult to distinguish between real and fake news. In this study, classification models for fake news detection were designed using machine learning algorithms. The dataset, which includes fake and real news examples, contains 42,000 examples. Each class, including fake and real samples, contains 22,000 sample data. In order to increase data quality, accuracy and usability, preprocessing methods were applied to the data set. The removal of numbers, stop words, and html tags was done in the pre-processing step to remove unnecessary information from the text. Models were created for fake news detection with singular and ensemble classification algorithms. Performance evaluation of the models was performed using 5-fold cross-validation. In the performance comparisons of the models, values such as accuracy, sensitivity, specificity, tp rate and fp rate were calculated. The highest performance results were observed in the random forest classification algorithm with an accuracy rate of 76%.

Do LSTM memory states reflect the relationships in reduced-complexity sandy shoreline models

Equilibrium-based models are a transparent method of modelling shoreline change, though often too simplistic to capture complex dynamics. Conversely, deep learning methodologies offer greater predictive power at the expense of transparency. In this research we scrutinize the internal workings of an LSTM shoreline model. A regression-based probe is used to show that cell state vectors, responsible for past-to-future information flow, autonomously generate equilibrium-like information akin to the physics-based equilibrium term of the ShoreFor model, Ωeq. The variation in probe skill throughout training is tracked to show that at 5 of 6 transects, the LSTM was able to meaningfully acquire equilibrium information (ΣΔR2 = 0.3–0.6). The results of this work offer evidence that an LSTM may model shoreline change with internal methods that are consistent with the current understanding of coastal shoreline dynamics. These physically meaningful representations emphasize the importance of co-evolution between machine learning and physics-based approaches moving forward.

Predictive Patterns and Market Efficiency: A Deep Learning Approach to Financial Time Series Forecasting

This study explores market efficiency and behavior by integrating key theories such as the Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH), Informational Efficiency and Random Walk theory. Using LSTM enhanced by optimizers like Stochastic Gradient Descent (SGD), Adam, AdaGrad, and RMSprop, we analyze market inefficiencies in the Standard and Poor’s (SPX) index over a 22-year period. Our results reveal “pockets in time” that challenge EMH predictions, particularly with the AdaGrad optimizer at a size of the hidden layer (HS) of 64. Beyond forecasting, we apply the Dominguez–Lobato (DL) and General Spectral (GS) tests as part of the Martingale Difference Hypothesis to assess statistical inefficiencies and deviations from the Random Walk model. By emphasizing “informational efficiency”, we examine how quickly new information is reflected in stock prices. We argue that market inefficiencies are transient phenomena influenced by structural shifts and information flow, challenging the notion that forecasting alone can refute EMH. Additionally, we compare LSTM with ARIMA with Exponential Smoothing, and LightGBM to highlight the strengths and limitations of these models in financial forecasting. The LSTM model excels at capturing temporal dependencies, while LightGBM demonstrates its effectiveness in detecting non-linear relationships. Our comprehensive approach offers a nuanced understanding of market dynamics and inefficiencies.

Exploring Communication Barriers in Bridge-Teams: An Innovative Fuzzy-Bayesian Approach

The bridge teams on merchant vessels have a grave responsibility to guarantee the safe navigation and management of ships in the critical waterways of the world. In addition to maintaining effective communication between external stations (other ships-Vessel Traffic Service), it is crucial to ensure continuous internal collaboration among the bridge team in order to fulfil this important task to the highest standard. Nevertheless, the challenging working conditions and harsh environmental factors may impede the uninterrupted flow of information between bridge teams and disrupt the communication. Communication issues among team members are frequently mentioned as a root cause in maritime accident investigation reports. The aim of this research is to propose a novel model for identifying the factors that may cause to inadequate communication among bridge team members, employing a fuzzy Bayesian network (FBN) approach. As indicated by the findings, attitudinal and behavioural barriers exert a greater influence (43.3%) on communication than language barriers (41.5%), representing the most significant factors affecting communication. Environmental barriers and cultural barriers, on the other hand, have comparatively less impact, at 38.7% and 31.2%, respectively. The sensivity analysis also revealed that the root nodes exhibiting the highest degree of impacts were cultural barriers (31.2%), age differences (20.6%), and workplace issues (20.2%). The findings suggest that bridge communication refresher training programs are essential for the mitigation of the aforementioned barriers, and are expected to lead to the development of new strategies for the overcoming of these communication barriers.

“The Throttling of the Free Press in Cuba”: Exploring Transnational Journalism and Sigma Delta Chi’s Advocacy for Press Freedom in Cuba, 1959–1962

ABSTRACT Members of Sigma Delta Chi (SDX), the largest journalism professional organization based in the United States, engaged in advocacy for transnational freedom of information and press freedom in Cuba between 1959 and 1962, between the Cuban Revolution and the Cuban Missile Crisis. Analysis of archival sources—including SDX member records, correspondence, and declassified government reports—and content in the official SDX magazine, reveals three themes: justifying a Latin America section with a primary focus on Cuba, the throttling of the free flow of information in and from Cuba, and documenting transnational flows of propaganda from Cuba and news from United States-based Cuban exiles to Cuba. Communist threats to reporters and democratic news organizations increased, and news management from the Cuban government and US government influenced information flows during this period. These findings add to the histories on free press advocacy abroad, cross-border journalism, and international press-politics in the context of US and Cuba relations.

Information and mathematical model of quantum communication channel state control processes

The most protected and stable communication systems today are quantum channels of information transmission and processing. Thanks to the unique properties of photons as information elements, it becomes possible to monitor and analyze the state of information flows in communication or information transmission channels. Physical attributes such as spin, polarization, radiation frequency, phase synchronization, and the quantum entanglement effect can be tracked and interpreted online to improve the quality and reliability of information in computer systems. In order to effectively use information in support or decision support systems, it is necessary to carefully formalize the processes and indicators of quantum systems for the creation of information processing and transmission, for which information and mathematical models should be created that describe the state of the quantum communication channel (QCC).The information model should allow the convolution of the information space. The mathematical model must prove the processes of tracking the states of quantum information and provide a description of the phase state of indicators of the quantum environment. A lock in a closed space with established cause-and-effect relationships is equal to a system of clear logic.The authors summarize the experience of developing and implementing the method of simulated dynamic modeling of events in an abstract communication channel, which allows formalizing and classifying cause-and-effect relationships of quantum carriers in the analyzed channels. It is proposed to use a unified neural network for the organization of SPPR in quantum-mechanical information transmission systems. Such a network could provide an automatic intelligent system state analysis mode. Such an analysis makes it possible to classify the aggregates of current system parameters to the level of diagnostics of the state of information flows and conclusions based on such diagnostics with the support of decision-making about the quality and reliability of the transmitted information. Such a system, working in OLAP (Online analytical processing) mode, could automatically manage the process of generation and transmission of information, reacting without human intervention to emerging critical errors or attempts at unauthorized system hacking. The observer effect leads to the fact that an attempt to measure the state of a photon inevitably causes an almost instantaneous change in this state. Attempting to parallelize a photon has the same consequences. This cannot be unnoticeable during further authorized acceptance of information. The analyzed quantum communication channel (QCM) consists of a set of technological elements distributed in space. The channel works in its own time, which is formed by clock pulses and creates a flow of information.

Maintaining the Free Flow of Information. A Manifesto-like Intervention for Practice-Based Research in Academic Training Programs for Multimedia Journalism

With a changing media landscape in mind, this article takes a closer look at academic training standards for journalists. Focusing on multimedia production and innovative science, it analyses the impact on business models, resources and working conditions. As an interventionist appeal, it argues in favour of practice-based research and new training methods. Key demands include a greater awareness of the democratic role of journalism, ethical sensitivity and sustainable funding. By interweaving theory, practice and politics, this Manifesto-like paper aims to strengthen the profession of journalism and build a bridge between academia and practical training.

THE USE OF ARTIFICIAL INTELLIGENCE IN ENTERPRISE MANAGEMENT AND IN MAKING MANAGEMENT DECISIONS IN MODERN CONDITIONS

This article provides an analysis of the use of artificial intelligence systems in various areas of enterprise management. The positive aspects, as well as the problems of introducing artificial intelligence, are considered. Artificial intelligence is used in a wide range of business tasks, allowing automation of many aspects of management activities. This determines the need to study and research its interaction with management processes in the company. This paper provides an analysis of literary sources devoted to research in the field of the use of artificial intelligence, presents its evolutionary development in the activities of enterprises, identifies types of organizations depending on the degree of use of artificial intelligence, and outlines the scope of its application in the business structure of companies. One of the main trends in the world economy at the present stage is the use of artificial intelligence systems in enterprise management, in the process of making management decisions under conditions of uncertainty. The further development of entrepreneurship, ensuring sustainable economic growth, determines the need to study scientific and methodological and scientific and methodological issues of implementing management decisions. The updated methodology for making strategic and management decisions should be focused on the harmonization of known and newly emerging techniques. Some experts note the emergence of signs of an information crisis - a discrepancy between the rapidly increasing mental abilities of people from generation to generation and the even more rapidly increasing flow of information. Currently, in the context of accelerating technological development in the world, the speed of change is rapidly increasing, the time of the business cycle from idea to profit has been reduced from several years to several months, and companies do not need to have material assets to operate effectively. In order to survive in new conditions, companies need to improve the quality of their strategic decisions, and such decisions must be fast, flexible and analytically sound. This development path should be based on intelligent ways of managing the most important resource in the 21st century - information. Artificial intelligence is becoming an essential element of management in many organizations, completely changing the functions and mechanisms of management activities, decision-making systems, human relations and communications in companies. Therefore, the issue of introducing artificial intelligence systems into management is becoming particularly relevant today. Today, in the digital economy, many enterprises are transforming, growing and entering new markets. To maintain the pace of business development at a high level, a flexible and modern approach to management is required. In this regard, the introduction of new technologies in management services is becoming increasingly important. Keywords: artificial intelligence, strategic management, business models, management decisions.

The Weakening of Taxpayer Rights in the Exchange of Information between Tax Authorities

Developments in international legislation and the growing digitalization of tax law support the development of global networks between tax authorities. We are witnessing an integration of databases that will lead to increasingly intense coordination of the fight against tax evasion at a supranational level. As in social networks and digital commerce, databases are increasingly enriched, contain increasingly precise information on the individual taxpayer and use common languages that allow for automated exchanges of information. While waiting for the creation of a global database – not conditioned by the constraints of reciprocity and abstractly usable by all authorized entities who need it – the first risks of limitation of the taxpayer’s rights are emerging. In fact, these phenomena have dark sides that are starting to emerge in use, at a national level and with respect to individual taxpayers, of interpolated databases. Moreover, a growing amount of information flows from heterogeneous and increasingly widespread sources, sometimes not protected by the requirements of professionalism, legality and public trust since data collection and entry can be delegated to economic entities, intermediaries and consultants. The absence of an authority responsible for the unitary management of global databases and for the resolution of their conflicts, the slow and timid affirmation (only in some national systems) of the taxpayer’s right of access to information concerning them, the difficult configuration of the faculty to request the correction of erroneous data and of the specular public power to remove the reported inaccuracies, weaken the system of protections gradually erected to protect the taxpayer’s position. In this way, the coordination of national systems that contemporary tax law creates is strongly unbalanced on the side of the protection of tax interests. International and European tax law should instead provide greater guarantees in favor of the taxpayers, defending their right to fair taxation.

DIGITAL INFORMATION’S INFLUENCE ON FORMING TOURISM EVENT IMPRESSIONS: THE INTERPLAY OF GENDER AND TOURISM TYPES

Digital information plays a crucial role in shaping tourists' expectations and impressions of potential destinations and activities. This study explores how perceptions of digital information vary across different tourism experience types (adventure, leisure, cultural) and examines the complex interactions between digital information perception and tourism engagement. A survey was conducted among tourists actively participating in various tourism types to collect data on their digital information consumption habits and their influence on their overall perceptions. The findings reveal that leisure and adventure tourists demonstrate a stronger initial reliance on digital information compared to cultural tourists. However, all tourism types exhibit negative interaction effects, suggesting diminishing returns of digital information as engagement increases. Specifically, leisure and adventure tourism show stronger positive main effects on digital information perception compared to cultural tourism. Interestingly, leisure and adventure tourism also display more pronounced negative interaction effects than cultural tourism. These insights suggest that destination marketers should employ nuanced, personalized strategies catered to specific tourism types, considering the varying levels of digital information reliance and the changing needs of tourists as their engagement progresses. For leisure and adventure tourism, this may involve front-loading comprehensive digital information, while cultural tourism may benefit from a more consistent information flow throughout the experience. These tailored approaches could enhance destination brand affinity and improve the overall tourist experience across different tourism segments.

Conceptual Model for Managing Sources of Danger in the Event of an Earthquake

Safety is defined as a state in which human activities are conducted in such a way that the realization of potential dangers is excluded with a certain probability. However, there is an established axiom: all human activities inherently carry some degree of danger. The criterion for assessing this danger is risk, which provides a quantitative measure of potential threats. Experience has shown that the previous concept of safety, which sought to eliminate all risks entirely (known as the "zero risk" concept), is not practical. Today, the prevailing safety approach is the concept of justified risk—the idea that certain levels of risk are deemed acceptable, based on current economic capabilities and social conditions, for the average citizen. This study presents the development of a conceptual model—a model of communication channels—created through system analysis. This model captures the dynamics of information flows regarding the status of danger sources within a potential earthquake zone. The conceptual model is composed of functional operators: B1, B2, B3, B4, B5, B6, and B7. One key area of this research is the mathematical modeling of emergencies and the forecasting of their potential consequences. Such modeling is crucial for enhancing the effectiveness of protective measures and improving disaster response strategies.

PECULIARITIES OF INFORMATION AND ANALYTICAL SUPPORT IN DECISION-MAKING WITHIN GOVERNMENT BODIES

The modern world is increasingly entering the era of an information society, where the role of information becomes ever more significant. Information is accumulating rapidly and in large volumes, so quickly that people struggle to process and thoroughly comprehend the incoming information flow, leading to an overall reduction in usable information. In order to process relevant data effectively in the context of professional or other activities today, it is no longer sufficient to rely solely on traditional administrative experience to make informed management decisions. It is essential to incorporate scientific knowledge, comprehensive awareness, analytical and forecasting skills, as well as socio-political responsibilities, with an appropriate recognition of these processes. This is particularly relevant for public administration bodies—both central and regional levels of executive state power—and increasingly for local government authorities, which are gaining more authority as part of the decentralization process. In Ukraine, in particular, the ideology and framework for interaction between the state and society are changing, with an increased use of decentralization mechanisms that delegate specific functions and powers from state bodies (to varying degrees) to the level of territorial communities and even to civil society. The purpose of this article is to define the peculiarities of information and analytical support in the decision-making process within government bodies. The article systematizes approaches to information and analytical support in public administration. It identifies the subjects and objects of the information and analytical support system in state management processes and specifies the goals of such support. It justifies the need to highlight the key elements of the information and analytical support system for public administration. The article also outlines the principles guiding information and analytical activities conducted by government and local authorities. Levels of information and analytical work are established, taking into account the temporal dimension. Furthermore, the article identifies issues with information and analytical support in government bodies in Ukraine and suggests ways to address these challenges.

The Types and Motives of Maxim Infringement in Agatha Christie’s Murder on The Orient Express Novel (Pragmatic Approach)

This study performs a pragmatic analysis of Agatha Christie’s "Murder on the Orient Express," focusing on the intentional types and motives behind Grice’s conversational maxims infringement strategies. This research is descriptive qualitative that uses document analysis from the dialogues where the characters strategically infringe the maxims and find the motive(s) behind every infringement. The motives are namely withholding information, providing misleading details, using ambiguity, and creating communicative effects. This research uses domain, taxonomy, componential analysis, and cultural theme as well as inductive technique for the data analysis technique. The results show that there are 118 infringements of the maxims that fulfill the types of all the maxim of quality, quantity, manner, and relevance as well as the motives This research finds out that the story is dominated by the infringement of the maxim of relevance to control information flow which aligns with the novel’s purpose as a crime-solving detective novel.

A progressive segmentation network for navigable areas with semantic–spatial information flow

Segmentation of safe navigable areas is a crucial technology for scene parsing in autopilot systems. However, existing segmentation methods often fail to adequately exploit the complementary relationships between multiscale features in complex wild environments, resulting in insufficient information fusion. In pursuit of a resolution, a Progressive Segmentation Network (PSNet) is proposed for navigable areas segmentation, which builds a semantic–spatial information flow branch to dynamically exploit the complementary relationships between multiscale features for progressive guided learning. Specifically, PSNet consists of four essential modules, including the Local Capturer and Global dependence Builder (LCGB), Multi-Directional Pooling Module (MDPM), Fusion-wise Module (FWM), and Spatial Weight Aggregation Module (SWAM). To facilitate efficient information dissemination, the LCGB was utilized to capture dense spatial information, and the MDPM was proposed to extract global geometric information of obstacles. These pieces of information serve as prior knowledge to guide learning. Additionally, we propose the FWM based on attention fusion unit (AFU) and contribution weights unit (CWU) to construct the complementary relationships between multiscale features and obtain rich multiscale fusion information. SWAM is proposed to enhance the significant spatial features from FWM and achieve impressive segmentation results. Extensive experimental results on the wild datasets demonstrate that PSNet outperforms state-of-the-art methods in recognizing safe navigable areas. The code for PSNet will be available at https://github.com/lv881314/PSNet.

ПРИМЕНЕНИЕ ТЕХНОЛОГИИ КОМАНДНОГО МЕТОДА ОБУЧЕНИЯ В ПРЕПОДАВАНИИ ФИЗИКИ В ШКОЛЕ

The article examines the technology of team learning used in teaching physics at school, as well as its application in pedagogical practice. It was noted that the growth of information flow and the development of technologies in modern society make it possible to integrate new teaching methods into the field of education. Attention was also drawn to the fact that the technology of team learning is based on a methodology that assumes that students search for information on their own. The article presents the technology of the team learning method and its stages, forms of student assessment and examples of the application of the group learning method in physics lessons, including working in groups on projects, conducting virtual laboratory work. Topics were discussed and samples of projects for 9th grade students in physics were presented. The advantages of the methodology were considered, such as increasing student independence, increasing interest in the subject and the difficulties faced by teachers associated with the introduction of team learning technology into the educational process.

Taking the Piss? Using Lean to Review the Process for Provision & Delivery of Continence Disposable Products

Abstract Background Urinary incontinence (UI) is the unintentional passing of urine. Its impact on older people varies from a diminished quality of life, to social isolation and frailty. Pads or diapers are commonly employed tools in the care of older people with incontinence. Incontinence is largely underreported and poorly managed among older people in community care settings (Yan et al., 2022). This work reviews the process map of the provision and delivery of continence disposable products in the community by the Health Service Executive in Ireland. Methods Lean is a quality improvement approach that consists of the elimination of waste to improve the flow of people, information or goods. We reviewed the process map baseline assessment; captured steps that added value to the patient and the impact on staff in the community. Results The initial assessment tool consists of five pages. There are 28 possible steps presented in the process map. There is a gap in the presentation on connectors to associated processes. Eight people are involved in the ordering, approval and delivery process to the older person. The initial assessment and education add value to the patient. If adverse skin or complaints arise an older person can be referred to a continence advisor or the ADPHN. The patient feedback loop is missing following delivery no check-in for education is provided following initial use. Unclear who is the point of contact for the patient to raise issues. The only noted key performance indicator is the processing of a request to order is inputted in one working day. Objectives are not SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound.) Conclusion Further in-depth research is required to review if the right staff are making the approvals. Need to review KPIs throughout to improve process and SMART objectives. Perspectives of older people and GP supports require further investigation.

Neural Dynamics Associated with Biological Variation in Normal Human Brain Regions.

The processes involved in encoding and decoding signals in the human brain are a continually studied topic, as neuronal information flow involves complex nonlinear dynamics. This study examines awake human intracranial electroencephalography (iEEG) data from normal brain regions to explore how biological sex influences these dynamics. The iEEG data were analyzed using permutation entropy and statistical complexity in the time domain and power spectrum calculations in the frequency domain. The Bandt and Pompe method was used to assess time series causality by associating probability distributions based on ordinal patterns with the signals. Due to the invasive nature of data acquisition, the study encountered limitations such as small sample sizes and potential sources of error. Nevertheless, the high spatial resolution of iEEG allows detailed analysis and comparison of specific brain regions. The results reveal differences between sexes in brain regions, observed through power spectrum, entropy, and complexity analyses. Significant differences were found in the left supramarginal gyrus, posterior cingulate, supplementary motor cortex, middle temporal gyrus, and right superior temporal gyrus. This study emphasizes the importance of considering sex as a biological variable in brain dynamics research, which is essential for improving the diagnosis and treatment of neurological and psychiatric disorders.