Information Exchange Mechanism Research Articles

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.

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.

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.

Evaluating Service Satisfaction and Sustainability of the Afya Insurance Scheme in Kuwait: An Exploratory Analysis.

In 2014, the Kuwaiti parliament enacted the private health insurance law for Kuwaiti retirees ie, Afya to enhance service quality for retirees through the involvement of the private sector. The study aimed to comprehensively analyze Afya across three crucial dimensions: efficiency, inclusiveness, effectiveness, and service quality; delivery of services throughout the revenue management cycle; and its long-term sustainability in Kuwait. Conducted between June 2021 and August 2022, the study employed both web-based and hardcopy questionnaires, reaching a response rate of 78.79% from a random sample of 514 Kuwaiti beneficiaries holding Afya insurance cards. Exclusions were made for those who did not receive cards or declined participation, resulting in analysis of 405 completed surveys. The findings revealed that 95% of participants frequently utilized services from private hospitals, with a corresponding 78.79% satisfaction rate. However, when seeking medical services from government hospitals, respondents often opted to repeat procedures to ensure accurate results and diagnoses. Despite a strong endorsement for enhancing Afya's coverage plan (94%), over half of the respondents suggested a temporary suspension of the scheme to conduct thorough research and alleviate potential financial burdens on the government. Notably, a significant positive correlation (0.578, p < 0.01) was observed between beneficiary satisfaction and their experience with Afya coverage, underscoring the importance of aligning scheme operations with beneficiary needs. Addressing these disparities and improving access to healthcare services for retirees necessitate a thorough reformation of the Afya scheme. To address these challenges, the study recommends comprehensive reform through evidence-based research, enhanced information exchange mechanisms between public and private sectors, and broader coverage targeting younger demographics. These measures are crucial for ensuring the scheme's efficacy, sustainability, and alignment with the evolving healthcare landscape in Kuwait.

Optimizing Energy Storage System Operations and Configuration through a Whale Optimization Algorithm Enhanced with Chaotic Mapping and IoT Data: Enhancing Efficiency and Longevity of Energy Storage Stations

To enhance the charging and discharging strategy of the energy storage system (ESS) and optimize its economic efficiency, this paper proposes a novel approach based on the enhanced whale algorithm. Recognizing that the standard whale algorithm can sometimes suffer from local optima in high-dimensional multiobjective optimization, this study introduces chaotic mapping and individual information exchange mechanisms to address this challenge. The proposed algorithm explores optimal configurations for different energy device placements and capacities through encircling and bubble searches, evaluating various multiobjective functions for optimization. In addition, the algorithm refines both the system operation model and the ESS configuration model, with the objective function being the analysis of the average annual revenue of the ESS. Model testing results demonstrate that this algorithm yields more moderate energy storage (ES) capacity decay, extending operational time to 3,124 days and achieving a full-life cycle benefit of the ESS as high as 1,821,623.68 yuan. Also, our algorithm demonstrates high efficiency, with minimal test time (68.36 seconds) and quick optimization (0.031 seconds per cycle), regardless of problem complexity.

Fully Distributed Optimal Economic Dispatch for Microgrids under Directed Communication Networks Considering Time Delays

Distributed generation and demand-side management are expected to play a more prominent role in future power systems. However, the increased number of generations and load demands pose new challenges to optimal energy management in a microgrid. In this paper, an economic dispatch model for microgrids considering Traditional Generators (TGs), energy storage units, wind turbines (WTs), and flexible loads is established. To tackle the Economic Dispatch Problem (EDP) over directed communication networks, a fully distributed algorithm developed by leveraging a two-step state information exchange mechanism is proposed. In addition, by employing a fixed stepsize, the proposed algorithm demonstrates rapid convergence. Furthermore, our algorithm is well-suited for nonquadratic convex cost functions. Subsequently, we extend our algorithm to address imperfect communication scenarios. Even in the presence of arbitrarily large yet bounded time delays, our algorithm exhibits robustness. Finally, several numerical examples are given to verify the correctness and effectiveness of the developed results.

A multi-range spectral-spatial transformer for hyperspectral image classification

In recent years, the transformer-based approach becoming a hot research topic in hyperspectral image (HSI) classification tasks. However, most of these studies have focused on optimizing the model framework in pursuit of high-accuracy classification, with little attention to the composition of the input token sequence as an important factor affecting the performance of the transformer. Therefore, this paper further explores a novel token structure to strengthen the Transformer's performance for HSI classification tasks, based on which a Multi-Range Spectral-Spatial Transformer (MRSST) framework is developed. Specifically, a convolutional feature pre-encoder with two branches is designed to extract shallow features for each spectral channel separately. Then, a token generator is introduced to combine the shallow features with the raw spectral information to yield the token sequences with multi-range information. Finally, the tokens are input into the transformer encoders enhanced by a module that strengthens the information exchange between its mid-range and short-range semantic features. Experiments conducted on three well-known hyperspectral datasets demonstrate that the proposed multi-range composite token sequences and information exchange mechanisms significantly enhance the transformer's performance. Codes are released at: https://github.com/HyperSystemAndImageProc/Multi-Range-Spectral-Spatial-Transformer.

Improved deep learning image classification algorithm based on Swin Transformer V2

While convolutional operation effectively extracts local features, their limited receptive fields make it challenging to capture global dependencies. Transformer, on the other hand, excels at global modeling and effectively captures global dependencies. However, the self-attention mechanism used in Transformers lacks a local mechanism for information exchange within specific regions. This article attempts to leverage the strengths of both Transformers and convolutional neural networks (CNNs) to enhance the Swin Transformer V2 model. By incorporating both convolutional operation and self-attention mechanism, the enhanced model combines the local information-capturing capability of CNNs and the long-range dependency-capturing ability of Transformers. The improved model enhances the extraction of local information through the introduction of the Swin Transformer Stem, inverted residual feed-forward network, and Dual-Branch Downsampling structure. Subsequently, it models global dependencies using the improved self-attention mechanism. Additionally, downsampling is applied to the attention mechanism’s Q and K to reduce computational and memory overhead. Under identical training conditions, the proposed method significantly improves classification accuracy on multiple image classification datasets, showcasing more robust generalization capabilities.