Information Exchange Mechanism Research Articles

Data-Oriented Octree Inverse Hierarchical Order Aggregation Hybrid Transformer-CNN for 3D Medical Segmentation.

The hybrid CNN-transformer structures harness the global contextualization of transformers with the local feature acuity of CNNs, propelling medical image segmentation to the next level. However, the majority of research has focused on the design and composition of hybrid structures, neglecting the data structure, which enhance segmentation performance, optimize resource efficiency, and bolster model generalization and interpretability. In this work, we propose a data-oriented octree inverse hierarchical order aggregation hybrid transformer-CNN (nnU-OctTN), which focuses on delving deeply into the data itself to identify and harness potential. The nnU-OctTN employs the U-Net as a foundational framework, with the node aggregation transformer serving as the encoder. Data features are stored within an octree data structure with each node computed autonomously yet interconnected through a block-to-block local information exchange mechanism. Oriented towards multi-resolution feature data map learning, a cross-fusion module has been designed that associates the encoder and decoder in a staggered vertical and horizontal approach. Inspired by nnUNet, our framework automatically adapts network parameters to the dataset instead of using pre-trained weights for initialization. The nnU-OctTN method was evaluated on the BTCV, ACDC, and BraTS datasets and achieved excellent performance with dice score coefficient (DSC) 86.95, 92.82, and 90.61, respectively, demonstrating its generalizability and effectiveness. Cross-fusion module effectiveness and model scalability are validated through ablation experiments on BTCV and Kidney. Extensive qualitative and quantitative experimental results demonstrate that nnU-OctTN achieves high-quality 3D medical segmentation that has competitive performance against current state-of-the-art methods, providing a promising idea for clinical applications.

Robust graph mutual-assistance convolutional networks for semi-supervised node classification tasks

Graph convolutional networks (GCNs) have made substantial progress in semi-supervised learning. However, GCNs are sensitive to graph noise, making the reconstruction of imperfect graph structures essential for adapting GCNs to complex scenarios. Prevalent GCNs employ graph augmentation to generate reliable augmented graphs, thereby mining potential graph information. Since traditional GCN layers can only perform message passing independently within each graph, GCNs primarily concentrate on fusing information from the node embeddings independently learned by the reliable augmented graphs. Nevertheless, the neglect of cross-graph information exchange during message passing limits the model's inter-graph interactions ability. In this paper, we propose a GCN framework called Robust graph mutual-assistance convolutional networks (RamNet), which introduces cross-graph information exchange mechanisms into the GCN layers. First, RamNet employs a multi-metric topology augmentation strategy to generate a synthesized graph, thereby exploring more reliable information. Second, it learns inter-graph interaction information by achieving information exchange between the raw graph and the synthesized graph during message passing. Finally, in addition to utilizing a small amount of supervised information, it also captures rich self-supervision information by maximizing consistency relationship between input graphs. We conduct comprehensive experiments in both standard and noisy scenarios, highlighting the superior performance of RamNet.

Implementation of network management in child protection policy: Analysis of mobilization strategy in Makassar City

This study aims to examine network management in the implementation of child protection policies in Makassar City, with a focus on mobilization strategies implemented by the Office of Women's Empowerment and Child Protection (DP3A). The research method used is a qualitative approach with data collection techniques through interviews, observations, and document studies. Informants in this study include representatives from DP3A, Makassar Police, Institute for Public Policy Studies (LSKP), and other related institutions. The results show that the mobilization strategy implemented by DP3A Makassar City, which includes motivating and rewarding networks, utilizing digital platforms, and cross-sector collaboration, has succeeded in increasing the effectiveness of child protection policy implementation. This success is in line with network management theory. DP3A Makassar City has managed to gather and utilize various resources, strengthen cross-sector collaboration, and accelerate responses to child abuse cases. The use of digital platforms and continuous evaluation are also key factors in the success of child protection efforts in Makassar City. This research provides recommendations for strengthening inter-agency coordination and collaboration through the establishment of regular coordination forums, developing efficient information and data exchange mechanisms, and establishing an integrated referral system. Future research is expected to develop a network management model or framework that can be adapted and applied in the context of child protection at the local, regional and national levels in Indonesia.

Stepwise methods for more nuanced adoption analysis: a case study of harvest and post-harvest mechanization in Bangladesh

The adoption of agricultural harvest and post-harvest mechanization is crucial for addressing drudgery, food losses, climate vulnerability and food security. Despite considerable efforts by government and development partners to prioritize agricultural mechanization, labour-intensive manual (post-)harvest activities continue to dominate in Bangladeshi smallholder systems. Explorations of this has been limited by simplistic binary approaches that ignore the dynamic pathways to usage outcomes. Instead, we apply non-binary analytical methods to district representative data to highlight the value in moving beyond binary adoption analysis. Results highlight that a national (post-)harvest mechanisation rate of 74% is insufficient to capture the true adoption status, given substantial disparity exists across machinery and by district. Deeper exploration of temporal and spatial differences enable the identification of key trends that warrant further in-depth explorations, while only 46% satisfaction with extension systems highlights the need to re-evaluate key information exchange mechanisms. We conclude that there is a clear need for district and machinery specific policy arrangements if Bangladesh is to achieve (post-)harvest mechanisation objectives that aim to reduce food loses and enable greater food security nationwide.

Application on power system economic dispatch of marine predator algorithm improved by asymmetric information exchange

The solution to the economic dispatch (ED) problem for power systems allows the power sector to reduce operating costs. However, the ED problem is a complex nonlinear and nonconvex optimization problem whose solution requires powerful algorithms. We propose a new version of the Marine Predator Algorithm (MPA), called IMPA, for solving complex ED problems. The algorithm introduces an asymmetric information exchange (AIE) mechanism, which not only accelerates to escape of local optima but also enriches the diversity of search. In this work, 12 benchmark functions were used to test the performance of the proposed algorithm IMPA. Then, the IMPA was used to solve the ED engineering problem of power system containing of 6, 13, 40, and complex 140 units. The minimum and average costs searched by IMPA are 1657962.7265$/h and 1657962.7265$/h, and they are much lower than the results of the MPA and NMPA, which means that our proposed improved IMPA improves the performance of MPA for solving the economic dispatch problem of large-scale power systems. The results show that the solutions obtained by IMPA are more competitive than those of MPA and NMPA, which provides an additional solution for cost reduction of the power system.

Abnormal communication internet-connected vehicle platoon dynamical modeling and characteristics study

With the advent of the vehicle interconnection era, all the internet-connected vehicles (i.e. ICVs) can communicate with each other through the whole platoon, and great changes of the dynamical motion characteristics in traffic flow system is happening. The wireless communication system plays more and more important roles to keep traffic flow system stable and safe. However, communication failure is a common and unevitable phenomenon, and an ICV in a platoon will inevitably lose its connection with other vehicles sometimes. So it is necessary and meaningful to study ICV’s dynamical characteristics under abnormal communication conditions. The study firstly analyzed the information exchange mechanism among ICVs and classify communication failure into three modes. Secondly, by considering normal and abnormal communication conditions, a car-following model for internet-connected vehicle flows with bidirectional mean expected velocity field was proposed. Thirdly, the ICV platoon system stable state condition was deduced based on linear stability theory. Finally, full simulation logical scenarios were set to support the theoretical analysis results considering the ICV system’s stability and communication states comprehensively. As for the abnormal communication state simulations, the leading vehicle, last vehicle, and middle vehicle communication failure scenarios were selected as typical examples. The study conclusions are (i) for bidirectional communication ICV platoon system, forward-looking and backward-looking mean expected velocity field terms show different influences on its stability, (ii) sudden abnormal communication failures can dramatically destroy the vehicle platoon stability, and (iii) different vehicle position communication failure has different effects on the system stability.

INTERNATIONAL EXPERIENCE IN USING INNOVATIVE TOOLS OF STATE FINANCIAL CONTROL OVER ILLEGAL FINANCIAL FLOWS

Effective financial control often requires cooperation between countries and international organizations. The study of international experience contributes to the development of mechanisms of international cooperation and information exchange to combat illegal financial flows. The article examines the international experience of using innovative tools of state financial control to combat illegal financial flows. Emphasis is placed on the analysis of modern practices in countries with developed systems of financial control, such as the USA, Great Britain, Germany, France, and Japan. The main tools used to detect and prevent illegal financial flows are described, including automated transaction monitoring systems, analytical platforms based on artificial intelligence, as well as methods of a risk-based approach to auditing. Special attention is paid to the role of international cooperation and information exchange between financial institutions of different countries, which allows more effective detection and termination of activities related to money laundering and terrorist financing. The article also analyzes the legal acts that regulate the use of innovative financial control tools in different countries and their compliance with international standards, such as FATF recommendations. The challenges and risks associated with the introduction of the latest technologies into the financial control system, including issues of data confidentiality and protection of human rights, were studied. The analysis of indicators and methods of assessment of illegal financial flows was carried out, the formation of illegal financial flows in modern globalized conditions was analyzed, the experience of other countries with regard to state financial control and its effectiveness was studied, innovative approaches and technologies that could be used to improve the financial control system and the possibility of their implementation of financial control in Ukraine. In addition, the evaluation of the effectiveness of the proposed innovative tools in the fight against illegal financial flows was carried out and the need for the development of such an assessment methodology, which would allow measuring the real impact of new technologies on reducing the volume of illegal financial flows, was substantiated.

Coordination of medium-voltage distribution networks and microgrids based on an aggregate flexibility region approach

The large-scale integration of distributed energy resources (DERs) presents operational challenges for medium-voltage distribution networks (MVDNs) and microgrids (MGs) because the conventional centralized scheduling framework lacks of an effective information exchange mechanism for coordinating the scheduling between MVDNs and MGs while supporting privacy concerns. The present work addresses these issues by leveraging a convex hull-based aggregate flexibility region (AFR) associated with the scheduling capability of massive DERs to coordinate the scheduling between an MVDN and MGs efficiently with very limited information exchange. Specifically, the AFR associated with the DERs in an MG is constructed based on the convex hull fitting method, and coordinated scheduling is facilitated by introducing safety operation constraints for the MVDN based on the AFRs of the MGs. Moreover, the coordination model is transformed into a readily solvable quadratically constrained quadratic programming problem by applying second-order cone transformations. The results of numerical computations applied to an IEEE 33-bus test system demonstrate that the obtained AFRs effectively characterize the flexible scheduling capability of DERs, and the proposed coordinated scheduling mechanism between the MVDN and MGs reduces the network losses and voltage deviations of the MVDN, while preserving information privacy.

Nociceptor‐Enhanced Spike‐Timing‐Dependent Plasticity in Memristor with Coexistence of Filamentary and Non‐Filamentary Switching

AbstractIn the era of big data, traditional computing architectures face limitations in handling vast amounts of data owing to the separate processing and memory units, thus causing bottlenecks and high‐energy consumption. Inspired by the human brain's information exchange mechanism, neuromorphic computing offers a promising solution. Resistive random access memory devices, particularly those with bilayer structures like Pt/TaOx/TiOx/TiN, show potential for neuromorphic computing owing to their simple design, low‐power consumption, and compatibility with existing technology. This study investigates the synaptic applications of Pt/TaOx/TiOx/TiN devices for neuromorphic computing. The unique coexistence of nonfilamentary and filamentary switching in the Pt/TaOx/TiOx/TiN device enables the realization of reservoir computing and the functions of artificial nociceptors and synapses. Additionally, the linkage between artificial nociceptors and synapses is examined based on injury‐enhanced spike‐time‐dependent plasticity paradigms. This study underscores the Pt/TaOx/TiOx/TiN device's potential in neuromorphic computing, providing a framework for simulating nociceptors, synapses, and learning principles.

Primary Ovarian Insufficiency: A Case of the Cart before the Horse

Overt Primary Ovarian Insufficiency (POI) is a rare disorder characterized by a constellation of other rare conditions, posing significant diagnostic and management challenges. This disorder necessitates a global mechanism for information exchange, streamlined data sharing, dynamic international partnerships, and the integration of artificial intelligence to enhance diagnostic accuracy through next-generation sequencing and specific antibody testing. Empowering women with POI involves providing them with access to the best available evidence at the point of care and facilitating informed decisions throughout their health journey. A key strategy is transcending the traditional biomedical model by adopting a population-based approach that encourages global cross-sectional collaboration. Optimal care for women with POI is achieved through an integrated, multidisciplinary approach that leverages diverse expertise to evaluate evidence and update care practices effectively. Initiatives are also underway to establish a patient advocacy group, emphasizing the importance of early diagnosis in allowing women to adapt family planning or preserve fertility. The overarching vision for POI research and care aims to cultivate proactive international partnerships and data sharing, driven by the needs and experiences of women with Overt POI.

Delay model for the dynamics of information units in the digital environment

The digital revolution has transformed the exchange of information between people, blurring the traditional roles of sources and recipients as active and passive entities. To study this, we build on a publicly available database of quotes, organized as units of information flowing through media and blogs with minimal distortion. Building on this, we offer an innovative interpretation of the observed temporal patterns through a minimal model with two ingredients: a two-way feedback between sources and recipients, and a delay in the media’s response to activity on blogs. Our model successfully fits the variety of observed patterns, revealing different attention decays in media and blogs, with rebounds of information typically occurring between 1 and 4 days after the initial dissemination. More important perhaps, the model uncovers a functional relationship between the rate of information flow from media to blogs and the decay of public attention, suggesting a simplification in the mechanisms of information exchange in digital media. Although further research is required to generalize these findings fully, our results demonstrate that even a bare-bones model can capture essential mechanisms of information dynamics in the digital environment.

The area-based management tools coordination between IMO and BBNJ agreement regimes and its implications on vessel pollution control

The International Maritime Organization (IMO), as a specialized agency of the United Nations responsible for the safety and security of international shipping and the prevention of pollution from ships, has applied two main area-based management tools (ABMTs): the “Special Areas” established under the MARPOL 73/78; and the “Particularly Sensitive Sea Areas” (PSSAs) established under the IMO resolutions. The new Agreement under the United Nations Convention on the Law of the Sea on the Conservation and Sustainable Use of Marine Biological Diversity of Areas beyond National Jurisdiction (BBNJ agreement) stipulates the establishment of a comprehensive system of ABMTs to conserve and sustainably use areas beyond national jurisdiction. Strengthening coordination in the use of ABMTs established by the IMO and the BBNJ Agreement is important for vessel pollution control in the high seas. The IMO is a stakeholder for relevant proposals and consultations on proposals regarding the establishment of ABMTs in the BBNJ Agreements, and can provide information on the implementation of them. The Conference of the Parties (COPs) to the BBNJ Agreement can also make recommendations to the IMO and its parties to promote the adoption of special areas and PSSAs. This article respectively elaborates on the practices and effect of ABMTs of the IMO and explores the relevant rules of the BBNJ agreement and their enforcement. Then this article discusses the possible approaches for the ABMTs coordination between the IMO and the BBNJ agreement regimes and their implications on vessel pollution Control in the high seas. Overall, relevant rules of the BBNJ agreement shall be interpreted and applied in a manner that does not undermine relevant legal instruments of the IMO. Meanwhile, it is necessary to promote cooperation and coordination between the COPs to the BBNJ Agreement and the IMO under the idea of conserving ecosystem integrity, gradually forming a normal cooperation and information exchange mechanism.

CLSTM-SNP: Convolutional Neural Network to Enhance Spiking Neural P Systems for Named Entity Recognition Based on Long Short-Term Memory Network

AbstractMembrane computing is a type of parallel computing system (generally called P system) abstracted from information exchange mechanisms in biological cells, tissues, or neurons, which can process data in a distributed and interpretable manner. LSTM-SNP, the first model of long short-term memory networks based on parameterized nonlinear Spiking neural P systems, was proposed recently. However, a systematic understanding and leveraging of the LSTM-SNP model to address named entity recognition (NER) and other natural language processing (NLP) tasks are still lacking. The bottleneck of the NER task lies in the scarcity of data and the vague definition of entity edges. Most approaches center on dataset handling, and there have been few attempts to address the issue in Spiking neural P (SNP) systems. This paper proposes a model named CLSTM-SNP based on the LSTM-SNP, aiming to tackle the NER problem in the field of SNP systems for the first time. First, this study employs a CNN layer to obtain character-level characteristics. Second, GloVe word vectors are utilized as word representations. Third, the research employs the LSTM-SNP to analyze textual features. We subsequently studied CLSTM-SNP’s effectiveness in addressing NER problems on CoNLL-2003 and OntoNotes 5.0 datasets and compared it to the results of five other baseline methods. Our model CLSTM-SNP achieved a macro F1-score of 89.2 $$\%$$ % on CoNLL-2003 and 75.5$$\%$$ % on OntoNotes 5.0, respectively. The performance of CLSTM-SNP and LSTM-SNP indicates a great potential for handling named entity recognition or other sequential tasks in NLP.

Improving Acacia smallholder plantation value chains in Thừa Thien Huế cooperative alliance, Vietnam

Acacia plantations in Vietnam have an important role in supplying the wood processing industry and generating income for small-scale forest owners. However, it is proving difficult to improve their incomes and reduce the vulnerabilities inherent to current production systems. To address this situation, a quantitative and qualitative value chain analysis was undertaken to understand the products and profits from small-scale Acacia plantations in Thừa Thiên Huế province in Vietnam. The results show that Acacia plantations are profitable, require low levels of inputs and have a short minimum rotation time of five years. Harvesting and transport costs account for a large proportion of total production costs. Traders play an important role in linking producers and processors. Most of the pro- ducts traded are Acacia logs for chipping and processing into other products and for saw-wood to produce timber for carpentry and domestic uses. Acacia logs with larger diameters of over 15 cm have a higher added value for wood processing companies producing furniture for export. Vulnerabilities include the highly fragmented value chain with little coordination and few information exchange mechanisms, and differences between ethnic groups that suggest a need for culturally sensitive intervention on value chains. Different options to increase profits – particularly for smallholder plantation owners – include product diversification, chain integration, and increased production to achieve efficiencies of scale.

Innovation’s, and civil servants’ behavioral models’ influence on the formation of customs authorities’ and financial intelligence units' cooperation and document exchange institutional conditions

The article examines the influence of innovation on the development of institutional conditions for cooperation and document exchange between customs authorities and financial intelligence units, considering the civil servants' behavior patterns as innovative approaches implementers. Cooperation between customs authorities and financial intelligence units provides a critical foundation for ensuring the integrity of international trade and financial systems. In a time of rapid technological advancement, innovation plays a key role in shaping the dynamics of this collaboration. Individual and organizational factors in the behavioral models' formation in the field of relevant cooperation and ways to increase the efficiency of interdepartmental cooperation are substantiated. Particular attention is paid to the procedure for the exchange of documents between customs authorities and financial intelligence units, considering commodity and financial flows in the global economy, and the inconsistency of national and international law in the international trade field certain provisions. The opportunities and challenges of such cooperation, incentives, and the ability to exchange information between customs authorities and financial intelligence units are identified. The need to introduce innovative tools and information exchange mechanisms specifically designed to identify suspicious signals in trade and financial transactions that may indicate crimes in international trade or sanctions evasion is argued. Instead of viewing innovation as a potential threat, customs authorities should actively engage with innovative enterprises to create mechanisms that consider technological advances. Such collaboration can lead to the development of systems and processes that not only ensure compliance with regulatory requirements, but also facilitate more convenient and efficient cross-border trade. The development of digital ecosystems opens promising opportunities for cooperation between customs and financial intelligence units. Such systems not only improve the efficiency of risk assessment, but also ensure financial crimes timely detection. This innovative approach creates a dynamic system that adapts to the changing nature of threats in global trade and finance. Keywords: innovations, behavior patterns of civil servants, customs authorities, financial intelligence units, cooperation, document exchange.

Coordinated Operation of Virtual Coupling High-Speed Trains Under a Novel Sequential Model Predictive Control Framework

This paper deals with the coordinated control problem of the multiple high-speeds operating under the virtual coupling mode with special consideration of the coupled safety inter-train distance constraints and other independent constraints affecting train dynamics. Considering the inertial lag phenomenon within longitudinal dynamics, this study employs a third-order nonlinear control model to characterize train motion. To cope with the intricate running constraints explicitly, a centralized constrained model predictive (MPC) control problem is formulated for the regulation of the trains. Within the framework of sequential solving approach, a novel distributed model predictive control (DMPC) algorithm is designed based on a distributed train running information exchange mechanism. The algorithm decomposes the centralized MPC problem into a sequence of local problems, allowing for sequential computation and meeting the real-time control requirements. Notably, the algorithm mitigates solution conservatism arising from the distributed optimization mechanism by accounting for potential hypothetical control policies among a train’s topological neighbors when designing its control policy. The feasibility and effectiveness of the proposed results are rigorously confirmed through theoretical analysis and illustrated by numerical experiment results.

Strategic environmental assessment in Uzbekistan: current trends, challenges and future prospects

This study has examined Uzbekistan’s environmental assessment framework, focusing on the challenges impeding Strategic Environmental Assessment implementation, as well as how international practices and pilot initiatives can guide the establishment of a robust and sus-tainable domestic SEA system. Through the analysis of legal documents, reports and pilot action outputs, the study has identified deficiencies in decision making and inconsistent mainstreaming of ecological matters into strategic planning. Thus, the major challenges include limited SEA awareness, insufficient baseline data, and weak legal and regulatory setting. Pilot projects, such as the Charvak Free Tourist Zone SEA, had revealed tangible concerns, including difficulties in engaging local stakeholders, data gaps, and unclear inter-agency roles. While these do represent significant obstacles, they also offer valuable lessons learnt for forging a comprehensive SEA scheme. Although the absence of a strong legal basis remains a critical limitation, there exist opportunities to enhance coordination, oversight, and stakeholder inclusivity. Uzbekistan can build on international SEA trends associated with public engagement and climate risk evaluation, while also learning from regional and cross-border actions. Collaborations across Central Asia, particularly in managing shared water and biodiversity resources, render additional opportunities for harmonizing environmental governance. The key priorities include the adoption of SEA legislation, capacity building through training and awareness programs, and the establishment of national networking and information exchange mechanisms. These steps are vital for fostering sustaina-ble development, preserving natural resources, and building public trust in ecological governance. The study findings furnish actionable insights for policymakers and practitioners in Uzbekistan and other nations facing similar environmental assessment challenges.

Цифровая преступность: понятие, криминологическая характеристика, предупреждение (часть 2)

This scientific work is a continuation of a previously published scientific article: if the first part of the study revealed the concept and criminological characteristics of digital crime, then the second part of the work is devoted more to the prevention of digital crime. The analysis of such a concept as a crime committed using information (digital) technologies shows that in the scientific literature there are different points of view regarding the content of this concept, however, each of the definitions is more or less based on the norms of current legislation in the field of information. The key purpose of this scientific research and the tasks arising from this goal are formulated as the disclosure of the definition of digital crime, the description of its types and the presentation of common positions on the issue of digital crime prevention. The research methodology is based on the use of general scientific and private scientific methods of cognition, in particular, the method of induction, deduction, generalization, analysis, research of official information sources, the method of expert assessments, etc. The resolution of the issue of the pre-vention of such crimes is of the greatest practical importance. The largest amount of authority in the fight against digital crimes is vested in specialized subjects of the prevention of digital crimes. However, only all actors together can ensure the systematic implementation of all prevention measures. In the fight against digital crimes, the subjects of prevention must act together and build an effective mechanism for interaction and exchange of necessary information. Countering the crimes in question includes a full range of operational and technical, search, operational and investigative measures, including explanatory and preventive means of general and individual prevention of offenses, which are provided for by current legislation.

Моніторинг результатів функціонування будівельних підприємств як базис оцінювання економічної безпеки

The article monitors the results of the functioning of construction enterprises as a basis for assessing economic security. It is found that the situation in the construction market is deteriorating every year, there is a noticeable significant reduction in the number of construction enterprises, which is due to a number of factors, among which are: the military invasion of russia, economic instability, an increase in the cost of building materials, a high level of administrative barriers and regulatory requirements, as well as a shortage of qualified personnel due to martial law and the mobilization of workers. It is noted that the decrease in the volume of manufactured and sold products of construction enterprises of Ukraine in recent years, as well as the reduction in the number of operating enterprises and employed employees, certainly affects the financial results of the industry, which, in turn, negatively affects the financial results. It is specified that close cooperation with the State, regional and local authorities, as well as the Armed Forces is critically important to ensure the economic security of construction enterprises. Therefore, the management of enterprises should develop effective mechanisms for interaction, information exchange and coordination of actions in order to minimize risks and quickly respond to problems. It is also necessary to have crisis management plans, which will become the basis for ensuring the economic security of construction enterprises during the wartime. Such plans should include recovery strategies, risk management, and options for solving crisis situations. It is concluded that ensuring the economic security of construction enterprises during martial law requires an integrated approach and detailed strategic planning. The main elements of this are: creation of resource reserves; development of evacuation plans and measures to protect workers; developing cooperation with government agencies and military administrations, as well as developing plans for effective crisis management. Only thanks to the implementation of these measures, enterprises will be able to work stably and contribute to the recovery of the national economy, even in difficult and unpredictable situations.

Fit-For-Purpose Cadaster Architecture for Moroccan Land-Use Planner: Proposal and Perception

The influx of foreign investment in Morocco necessitates a robust and integrated land information system to support informed decision-making and ensure sustainable growth. The fit-for-purpose cadastre emerges as a promising solution, offering transparency, efficiency, and data-driven land management. However, its full potential remains untapped due to persistent challenges: fragmented data across government agencies, opaque information exchange mechanisms, and outdated infrastructure. This research delves into these challenges by investigating the perspectives of diverse stakeholders crucial to successful cadastre implementation: land tenure specialists, public policymakers, spatial planners, and investors. Through in-depth interviews, we illuminate the current state of land information management in Morocco, pinpoint critical roadblocks hindering transparency, efficiency, and sustainability, and propose a multi-pronged approach to unlock the transformative potential of a fit-for-purpose cadastre system.