Information Exchange Research Articles

Multi-scale anatomical regularization for domain-adaptive segmentation of pelvic CBCT images.

Cone beam computed tomography (CBCT) image segmentation is crucial in prostate cancer radiotherapy, enabling precise delineation of the prostate gland for accurate treatment planning and delivery. However, the poor quality of CBCT images poses challenges in clinical practice, making annotation difficult due to factors such as image noise, low contrast, and organdeformation. The objective of this study is to create a segmentation model for the label-free target domain (CBCT), leveraging valuable insights derived from the label-rich source domain (CT). This goal is achieved by addressing the domain gap across diverse domains through the implementation of a cross-modality medical image segmentationframework. Our approach introduces a multi-scale domain adaptive segmentation method, performing domain adaptation simultaneously at both the image and feature levels. The primary innovation lies in a novel multi-scale anatomical regularization approach, which (i) aligns the target domain feature space with the source domain feature space at multiple spatial scales simultaneously, and (ii) exchanges information across different scales to fuse knowledge from multi-scaleperspectives. Quantitative and qualitative experiments were conducted on pelvic CBCT segmentation tasks. The training dataset comprises 40 unpaired CBCT-CT images with only CT images annotated. The validation and testing datasets consist of 5 and 10 CT images, respectively, all with annotations. The experimental results demonstrate the superior performance of our method compared to other state-of-the-art cross-modality medical image segmentation methods. The Dice similarity coefficients (DSC) for CBCT image segmentation results is %, and the average symmetric surface distance (ASSD) is . Statistical analysis confirms the statistical significance of the improvements achieved by ourmethod. Our method exhibits superiority in pelvic CBCT image segmentation compared to its counterparts.

ASF-Net: Robust video deraining via temporal alignment and online adaptive learning

In recent times, learning-based methods for video deraining have demonstrated commendable results. However, there are two critical challenges that these methods are yet to address: exploiting temporal correlations among adjacent frames and ensuring adaptability to unknown real-world scenarios. To overcome these challenges, we explore video deraining from a paradigm design perspective to learning strategy construction. Specifically, we propose a new computational paradigm, Alignment-Shift-Fusion Network (ASF-Net), which incorporates a temporal shift module. This module is novel to this field and provides deeper exploration of temporal information by facilitating the exchange of channel-level information within the feature space. To fully discharge the model’s characterization capability, we further construct a LArge-scale RAiny video dataset (LARA) which also supports the development of this community. On the basis of the newly-constructed dataset, we explore the parameters learning process by developing an innovative re-degraded learning strategy. This strategy bridges the gap between synthetic and real-world scenes, resulting in stronger scene adaptability. Our approach exhibits superior performance in three benchmarks and compelling visual quality in real-world scenarios, underscoring its efficacy. The code is available at https://github.com/vis-opt-group/ASF-Net.

Centralized vs. Decentralized Cloud Computing in Healthcare

Healthcare is one of the industries that seeks to deliver medical services to patients on time. One of the issues it currently grapples with is real-time patient data exchange between various healthcare organizations. This challenge was solved by both centralized and decentralized cloud computing architecture solutions. In this paper, we review the current state of these two cloud computing architectures in the health sector with regard to the effect on the efficiency of Health Information Exchange (HIE) systems. Our study seeks to determine the relevance of these cloud computing approaches in assisting healthcare facilities in the decision-making process to adopt HIE systems. This paper considers the system performance, patient data privacy, and cost and identifies research directions in each of the architectures. This study shows that there are some benefits in both cloud architectures, but there are also some drawbacks. The prominent characteristic of centralized cloud computing is that all data and information are stored together at one location, known as a single data center. This offers many services, such as integration, effectiveness, simplicity, and rapid information access. However, it entails providing data privacy and confidentiality aspects because it will face the hazard of a single point of failure. On the other hand, decentralized cloud computing is built to safeguard data privacy and security whereby data are distributed to several nodes as a way of forming mini-data centers. This increases the system’s ability to cope with a node failure. Thus, continuity and less latency are achieved. Nevertheless, it poses integration issues because managing data from several sites could be a problem, and the costs of operating several data centers are higher and complex. This paper also pays attention to the differences in aspects like efficiency, capacity, and cost. This paper assists healthcare organizations in determining the most suitable cloud architecture strategy for deploying secure and effective HIE systems.

Leveraging the use of a social media app as a collaborative medical information and knowledge sharing tool—insights from experiential use and survey of a CME WhatsApp community

Background and aimContinuing Medical Education (CME) is crucial for physicians to stay current in the rapidly advancing field of medicine. A WhatsApp (WA) based community of physicians was initiated in 2016 ‘WhatsApp CME India Group’ to facilitate learning, knowledge sharing, and discussion among physicians. Due to participant size constraints of the technology, it evolved into seven distinct WA groups, overseen by a central administrator. A survey undertaken in the group’s 7th year aimed to evaluate its effectiveness in achieving its goals, measure participant engagement, and uncover the primary factors driving its usage.MethodsThe survey questionnaire was distributed to 3500 members across the 07 WhatsApp CME groups for voluntary participation. Data collected was analysed using SPSS version 24.0 (SPSS Inc., Chicago, IL, USA) and reported using descriptive statistics.ResultsAmong the 581 survey respondents, the study found 43% of physicians use the WhatsApp academic groups for CME content, with 32% accessing their group over four times daily. The primary motivation for 77% was to discuss challenging cases and to gain knowledge updates from fellow physician (70%). Medical websites (57%), referral books (49%), and Google (43%) were other significant resources. Every participant (100%) found the WhatsApp CME India group the most beneficial resource for daily medical science updates. A significant portion (57%) of the respondents found the group valuable for real-time information exchange. Over 78% stated it kept them current with knowledge and guidelines. Notably, 94% viewed WhatsApp CME as complementary to physical conferences, not a replacement. The post-conference/webinar summaries were appreciated by 81% participants. Case discussions (31%) and update posts from fellow physicians sharing their insights and learnings (24%) were noted as activities of great academic interest.ConclusionThis study underscores the potential of digital platforms like WhatsApp in supplementing CME in India and potentially other comparable settings. The blend of digital and traditional resources suggests a balanced learning approach. While real-time engagement is a strength, challenges like information overload and privacy concerns require careful management. Striking a balance is crucial for ensuring content quality, structured discussions and privacy. As the digital age advances, professionals must critically assess shared information on these platforms to guarantee evidence-based and reliable knowledge dissemination.Trial registrationARK 001.

Management of Suspicions of Child Maltreatment at Child and Family Clinics: A Mixed-Methods Study

AbstractThis study investigated Norwegian public health nurses working in child and family health clinics’ decision-making practices concerning child maltreatment. A one-phase convergent mixed-method design was utilized, with quantitative and qualitative data concurrently collected and separately analyzed from a questionnaire that included patient vignettes. These vignettes described three potential child maltreatment scenarios to assess public health nurses’ competence and clinical decision-making processes. A total of 554 public health nurses participated in the study conducted from October 24 to December 31, 2022. Findings reveal a high level of recognition of child maltreatment among public health nurses. However, despite their self-perceived confidence, the qualitative data underscore the complexity and uncertainty inherent in their assessment process. The most supported management approaches involved further exploration of the situation. Few public health nurses would file a report of concerns to Child Protective Services or report the police despite their level of concerns. The findings underscore the necessity for additional training and support to enhance public health nurses’ confidence and competence in reporting suspected child maltreatment cases, as well as the need for increased information exchange regarding the procedures and services provided by the child if Child Protective Service is involved.

Centrality of Information Sources in Agricultural Input Networks: Insights from Social Network Analysis

Input dealers being credible localite sources of information for farmers have huge potential of aiding faster diffusion of innovations. Under this context, understanding how the input dealers’ access and exchange information is essential for enriching them with updated information and improving the agricultural value chain. This study employs Social Network Analysis (SNA) to explore the centrality measures—degree centrality, closeness centrality, and betweenness centrality—of various information sources utilized by input dealers in Nagar Kurnool and Wanaparthy districts of Telangana during 2020. The analysis reveals that traditional sources, such as extension agents and other input dealers, are highly central to the network, with both scoring the highest in degree and closeness centrality (0.733 and 0.698, respectively). These sources are not only frequently accessed but also serve as key bridges within the network, facilitating the flow of information. Digital platforms like smartphones show growing importance, with significant degree centrality (0.667), yet they play a less critical bridging role. In contrast, social media and print media exhibit lower centrality scores, indicating their peripheral influence. The findings suggest that while traditional information sources remain dominant, digital platforms are emerging as valuable tools for information dissemination highlighting the need for strategies that strengthen traditional channels while integrating digital tools to enhance the efficiency and reach of agricultural information networks. This research contributes to a deeper understanding of the complex information ecosystem in agriculture, offering insights for policymakers and stakeholders aiming to optimize information flows within the sector.

Developing a BIM based digital twin system for structural health monitoring of civil infrastructure

Abstract The utilization of building information modeling (BIM) within digital technology facilitates the creation of three-dimensional representations for monitoring data in large-scale civil infrastructure. In response to the need for intelligent structural management, this study establishes a structural health monitoring (SHM) system and foundational framework based on digital twins. This framework integrates information from various sources and facilitates collaborative efforts for structural operation and maintenance. Additionally, the SHM system integrates actual monitoring measurements and early warning mechanisms to consolidate multi-source monitoring data with BIM. Through real-time analysis, the system provides insights into the operational status of bridges, capturing geometric, physical, and performance evolution characteristics. To construct the system, engineering challenges are initially digitized, with appropriate sensors deployed on real bridge structures to monitor dynamic (acceleration) and static (strain, displacement) physical information during bridge operation. Subsequently, through wireless communication and data storage technologies, the monitored physical data serves as input for mode identification and early warning algorithms, facilitating the acquisition of structural performance information. Finally, three-dimensional display technology enables real-time calculation and rendering of BIM models, fostering the exchange and interaction of monitoring and BIM information, thus enhancing the intelligence of SHM system.

Blockchain-Based LSTM Approach for Secure Data Transmission in IoT-Enabled Healthcare System

Blockchain technology can help with a number of issues facing the healthcare sector. Its potential as trust brokers includes the provision of incentive machines and the facilitation of innovative healthcare solutions. Additionally, its potential as trust brokers includes the facilitation of novel business models that offer fresh perspectives on the interactions between patients and providers, among other diverse healthcare stakeholders. Blockchain, for instance, makes incentive structures easier to implement and promotes decentralised trust in patient-centered healthcare models and global health information exchange (HIE). On the other hand, blockchain is characterised as a decentralised network or service that can help remove obstacles in the healthcare sector. The healthcare sector has benefited greatly from the effective resolution of problems relating to adoption and technological obstacles, interoperability and integration with current systems, cost unpredictability, scale, and regulatory compliance through the use of Blockchain technology. This work proposed discusses fault tolerance techniques in relation to sharing health-related data.

Cryptocurrency and its Nexus with Money Laundering and Terrorism Financing within the Framework of FATF Recommendations

Cryptocurrency has emerged as a viable alternative to conventional payment systems, offering its users notable advantages such as cost efficiency and rapid transaction processing. However, cryptocurrencies’ decentralized nature, anonymity, and susceptibility to cyber threats introduce the potential for their exploitation in illicit activities, notably money laundering and terrorism financing (ML/TF). This scholarly investigation seeks to investigate the roles played by international organizations actively combating such criminal activities. In this academic context, a meticulous consideration of the inherent risks associated with the aforementioned criminal endeavors is undertaken, aligning with the guidelines and recommendations set forth by the Financial Action Task Force (FATF). The study underscores a significant finding that cryptocurrency accounts can be opened anonymously, with no centralized registry to monitor cryptocurrency ownership. This characteristic poses a formidable challenge in confiscating funds linked to terrorist activities. Even in instances where cryptocurrency transactions may be traced, accessing this critical data necessitates the involvement of third-party entities, given that cryptocurrency transactions are recorded in decentralized ledgers spanning multiple jurisdictions. Furthermore, the study underscores the imperative of coordination and information exchange as indispensable elements in the ongoing battle against organized and transnational criminal activities, specifically ML/TF. Moreover, the study elucidates the incongruence between cryptocurrency and FATF regulations governing electronic fund transfers, as cryptocurrencies can make transactions through opaque and unregulated conduits, such as the deep web.

Responsible metaverse: Ethical principles for guiding decision-making and maintaining complex relationships for businesses in 3D virtual spaces

A metaverse is a three-dimensional virtual space (3D VS) where businesses and individuals worldwide can engage, interact, communicate, transact, and exchange information in real-time through an immersive and collaborative platform. These interactions can create complex relationships influenced by the decision-making processes of businesses. Such complexity can lead to challenges in maintaining relationships, ensuring exclusiveness, preventing misuse, and addressing other ethical issues. Therefore, this study aims to identify ethical principles within the metaverse to guide decision-making and maintain complex relationships between users and businesses. Both qualitative and quantitative data were collected for analysis, and simple random sampling was employed for primary data collection. The empirical analysis was conducted using a mixed-method approach. The study identified four ethical principles that guide complex relationships within the metaverse: business benefit evaluation, fairness, explainability, and reliability principles. These principles positively influence decision-making, which, in turn, positively affects the maintenance of complex relationships within 3D VS.

Stakeholder engagement: From sharing information and building consensus to mobilising public and private actors to create collaborative ecosystems

The paper proposes a novel approach to dialogue with local stakeholders to maximise the shared value that can be generated by infrastructure investments for the benefit of urban communities. The approach proposes a method for deliberately activating synergies between infrastructure and complementary initiatives through the active engagement of public and private stakeholders. In many cases today, infrastructure is designed, financed and implemented as functionally unitary engineering works that meet a specific need, leaving synergies with complementary initiatives to be activated spontaneously. Moreover, the dialogue with local stakeholders is limited and reductive, aiming mainly at the exchange of some project information and consensus building, thus missing the opportunity to identify and mobilise those ideas, capabilities and resources that are always present within a community. In the proposed approach, infrastructure is the catalyst for a long-term vision and stakeholder engagement is the tool for identifying the synergistic initiatives that can be implemented with the participation of public and private actors to maximise value creation.

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.

Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium- and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid- and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.

Stakeholder engagement: From sharing information and building consensus to mobilising public and private actors to create collaborative ecosystems

The paper proposes a novel approach to dialogue with local stakeholders to maximise the shared value that can be generated by infrastructure investments for the benefit of urban communities. The approach proposes a method for deliberately activating synergies between infrastructure and complementary initiatives through the active engagement of public and private stakeholders. In many cases today, infrastructure is designed, financed and implemented as functionally unitary engineering works that meet a specific need, leaving synergies with complementary initiatives to be activated spontaneously. Moreover, the dialogue with local stakeholders is limited and reductive, aiming mainly at the exchange of some project information and consensus building, thus missing the opportunity to identify and mobilise those ideas, capabilities and resources that are always present within a community. In the proposed approach, infrastructure is the catalyst for a long-term vision and stakeholder engagement is the tool for identifying the synergistic initiatives that can be implemented with the participation of public and private actors to maximise value creation.

Coming clean on your taxes

This paper develops a simple model to explore whether a higher detection probability of tax evaders—e.g., because of improved exchange of information between countries and/or due to digitalization of tax administrations—renders it optimal for governments to introduce a voluntary disclosure program (VDP) and, if so, under what terms. We find that if the VDP is unanticipated, it is likely to be optimal for a revenue-maximizing government to introduce a VDP with relatively generous terms, i.e., a low or even negative penalty. When anticipated, however, the VDP is neither incentive compatible nor optimal, as it induces otherwise compliant taxpayers to evade tax. A VDP can then only be beneficial if tax evasion induces an external social cost beyond the direct revenue foregone, e.g., due to adverse effects on overall tax morale. In contrast to the common view that VDPs should come along with additional enforcement effort, we find that governments should relax enforcement if the VDP itself provides more powerful incentives to come clean.

The role of the unified communications platform in the implementation of the third mission by a modern university

Introduction. The third mission of the university involves intensive communication, which is an indispensable means of solving social, economic and environmental problems of the region of its location. Innovative elaborations for sustainable development require a transition from disparate services to unified communications (UCaaS). The purpose of the study is to identify the potential of the unified communications platform for the exchange of information between the university and its stakeholders, which allows us to see new ways to implement the social mission of "University and Society". Materials and methods. Twelve department heads of Novosibirsk State Technical University were interviewed on the most demanded characteristics of a communication platform for achieving managerial, organizational and educational goals within the framework of university activities. Three experts from UCaaS platform development companies were also interviewed about the obstacles that arise in the way of introducing this technology into the university environment. The data was processed and visualized using the bar chart method. Results. The most demanded options of UCaaS platform satisfying the objectives of the university's third mission are the control of student engagement (25% of respondents), collaboration on documents (19%) and information security (17%). Tools for working in a distributed team are also relevant (15%). Barriers to the introduction of the platform into the digital environment of universities include the lack of necessary infrastructure, the conservative format of classes, and the lack of a continuing education system. The main problems of relations with stakeholders are the episodic nature of contacts, the insufficiency of their planning and data analysis, the lack of mechanisms for the influence of stakeholders on the university management processes. Conclusion. The passive-expectant style of the university's behavior in the educational services market contradicts the vectors of the implementation of the university's third mission, while being a problem of integrating UCaaS into the higher school system. The model of expanding the university's cooperation with stakeholders forms the university's communication ecosystem, initiates productive networking for joint projects. The UCaaS platform's services are effective both for creating social engagement and for technology transfer and continuing education.

Contextualised digital health communication infrastructure standards for resource-constrained settings: Perception of digital health stakeholders regarding suitability for Uganda's health system.

Ignoring the need to contextualise international standards has caused low-resourced countries to implement digital health systems on the ad-hoc, thereby often failing to meet the local needs or scale up. Authors have recommended adapting standards to a country's context. However, to date, most resources constrained countries like Uganda have not done so, affecting their success in attaining the full benefits of using ICT to support their health systems. They apply the standards 'as is' with little regard for their fitness for potential use and ability to fulfil the country's digital health needs. A design science approach was followed to elicit digital health communication infrastructure (DHCI) requirements and develop the contextual DHCI standards for Uganda. The design science methodology's design cycle supported DHCI standards' construction and evaluation activities. Whereas two workgroup sessions were held to craft the standards, three cycles of evaluation and refinement were performed. The final refinement produces the contextualised DHCI standards approved by Uganda's DH stakeholders through summative evaluation. Results of the summative evaluation show that DH stakeholders agree that the statement of the standards and the requirements specification are suitable to guide DHCI standards implementation in Uganda. Stakeholders agreed that the standards are complete, have the potential to realise DHCI requirements in Uganda, that have been well structured and follow international style for standards, and finally, that the standards are fit to realise their intended use in Uganda. Having been endorsed by DH stakeholders in Uganda's health system, the standards should be piloted to establish their potency to improve health information exchange and healthcare outcomes. Also, we recommend other low middle income countries (LMICs) with similar challenges to those in Uganda adopt the same set of contextualised DHCI standards.

Decentralized Learning Control for High-Speed Trains With Unknown Time-Varying Speed Delays

Treating the multi-point-mass dynamic model of high-speed trains as an interconnected system, this study proposes a decentralized iterative learning control scheme for high-speed trains to achieve the trajectory tracking goal. By making reasonable estimates of the interaction term and compensating for it, the proposed control scheme utilizes only local information from each carriage and does not need any inter-carriage information exchange. The zero-error tracking of the desired trajectory is guaranteed even in a restricted communication environment. Considering unknown time-varying speed delays in the actual high-speed train operations, a modified decentralized iterative learning control scheme is also provided to address the negative impact of speed delays. The convergence of tracking errors is strictly proven by constructing appropriate composite energy functions. Numerical simulations further verify the theoretical results.

Integration of distributed PV into smart grids: A comprehensive analysis for Germany

Modern smart grids typically combine physical and communication networks for efficient information exchange and innovative applications. Aligned with digitalization and advancements in smart grids, the integration of photovoltaic (PV) systems comprises a variety of regulatory and technological aspects. However, no previous study has conducted an extensive and systematic analysis of the PV-grid integration framework, particularly for one country. To fill this gap, this paper uses Germany as an example to present a comprehensive, state-of-the-art analysis of integrating distributed PV systems into smart grids, focusing on the regulation and technical implementation of the German Smart Meter Infrastructure and PV control interfaces. Starting from a standardization perspective, this analysis utilizes the Smart Grid Architecture Model to identify crucial roles, components and processes specifically in Germany. Furthermore, it outlines the current implementation of PV integration into distribution networks at a national level. The results of this study show the overall complexity of PV integration in the smart grid context, confirm the feasibility of the German integration approach, and highlight the necessity of deploying standardized information models and communication technologies. These key findings can help market participants with different roles to identify potential technical bottlenecks or other critical points in the regulation and technical implementation. For instance, the proposed in-depth analysis framework provides an orientation for characterizing the PV integration or, more generally, the grid integration scenario of renewables in other countries.

P022 A FEASIBILITY STUDY OF COMMUNITY SUPPORT GROUPS FOR NON-COMMUNICABLE DISEASE SELF-MANAGEMENT IN KATHMANDU DISTRICT OF NEPAL

Background: Non-communicable diseases (NCDs) are the primary contributors to mortality and, therefore, considered a major public health problem. Community support group (CSG) interventions are advocated as cost-effective strategies for managing NCDs like diabetes and hypertension. While these interventions are globally popular in fostering self-help and motivation for adopting healthy practices, their effectiveness and feasibility in Nepal are poorly assessed. This study aimed to explore the feasibility of creating community support groups to manage NCDs, particularly diabetes and hypertension; conducting regular meetings; and understanding the preliminary impact of CSG meetings on NCD self-management behaviors. Methods: A pre-post interventional study was conducted in Kathmandu district, Nepal, using a mixed-method approach. CSGs were formed for individuals aged 40 and above, diagnosed with hypertension and/or diabetes. Each of these groups convened twice monthly over three months, focusing on self-management practices related to diet, physical activity, medication adherence, self-monitoring, and foot care. Baseline and end-line surveys measured the initial impact, while qualitative data from meeting discussions and in-depth interviews captured patient perspectives and meeting acceptability. Quantitative analysis involved descriptive statistics, chi-square, and t-tests, while thematic analysis was applied to qualitative data. Results: Of the total 41 participants in the study, 80% (mean age: 60.1 years) were female. The attendance in the group meetings ranged from 70% to 90%. Post-intervention, significant improvements were noted in blood pressure and weight. Qualitative findings revealed strong acceptance of CSG meetings, fostering information exchange and shared learning that could motivate self-management behavior changes. While participants acknowledged potential challenges in regular meeting attendance, such as finding common interests for collaborative activities, most found facilitator-led sessions interesting and promising. Homogenous groups, due to shared demands, are more inclined to engage in collaborative activities. Conclusion: This study suggests the potential effectiveness of support group activities for self-managing NCDs in Nepal. Large scale randomized controlled trial are recommended to assess the long-term effectiveness and sustainability of this approach.

HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals.

Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehensive consideration of depression's pathogenesis, leading to limited neuroscientific interpretability. To address these issues, we propose a hemisphere asymmetry network (HEMAsNet) inspired by the brain for depression recognition from EEG signals. HEMAsNet employs a combination of multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) blocks to extract temporal features from both hemispheres of the brain. Moreover, the model introduces a unique 'Callosum-like' block, inspired by the corpus callosum's pivotal role in facilitating inter-hemispheric information transfer within the brain. This block enhances information exchange between hemispheres, potentially improving depression recognition accuracy. To validate the performance of HEMAsNet, we first confirmed the asymmetric features of frontal lobe EEG in the MODMA dataset. Subsequently, our method achieved a depression recognition accuracy of 0.8067, indicating its effectiveness in increasing classification performance. Furthermore, we conducted a comprehensive investigation from spatial and frequency perspectives, demonstrating HEMAsNet's innovation in explaining model decisions. The advantages of HEMAsNet lie in its ability to achieve more accurate and interpretable recognition of depression through the simulation of physiological processes, integration of spatial information, and incorporation of the Callosum-like block.