Information Flow Problem Research Articles

High-Efficiency and High-Precision Ship Detection Algorithm Based on Improved YOLOv8n

With the development of the intelligent vision industry, ship detection and identification technology has gradually become a research hotspot in the field of marine insurance and port logistics. However, due to the interference of rain, haze, waves, light, and other bad weather, the robustness and effectiveness of existing detection algorithms remain a continuous challenge. For this reason, an improved YOLOv8n algorithm is proposed for the detection of ship targets under unforeseen environmental conditions. In the proposed method, the efficient multi-scale attention module (C2f_EMAM) is introduced to integrate the context information of different scales so that the convolutional neural network can generate better pixel-level attention to high-level feature maps. In addition, a fully-concatenate bi-directional feature pyramid network (Concatenate_FBiFPN) is adopted to replace the simple superposition/addition of feature map, which can better solve the problem of feature propagation and information flow in target detection. An improved spatial pyramid pooling fast structure (SPPF2+1) is also designed to emphasize low-level pooling features and reduce the pooling depth to accommodate the information characteristics of the ship. A comparison experiment was conducted between other mainstream methods and our proposed algorithm. Results showed that our proposed algorithm outperformed other models by achieving 99.4% of accuracy, 98.2% of precision, 98.5% of recall, 99.1% of mAP@.5, and 85.4% of mAP@.5:.95 on the SeaShips dataset.

Impact of information on human health: Educational and professional aspects

Currently, the information space has a great influence on all types of human activity, allowing us to form a worldview, habits and skills necessary for life. However, global informatization and digitalization, affecting bodily, mental and social constructs, affects the duration and quality of life. The information a person receives affects his physical, mental and social well-being. The problem of security of information flows, especially in educational and professional activities, is relevant because it has a complex impact on the human body. The article examines the impact of information flows on human health and reveals the impact of the modern digital environment on a person’s physical, mental health and social wellbeing. The purpose of the work was to study the complex impact of modern information flows on individual and public health, as well as systematize measures aimed at reducing the negative effects of this process. There are general approaches to considering the problem of information flows and their impact on humans, which we are trying to present. A set of information hygiene and safety measures is proposed in the context of maintaining health, increasing the effectiveness of training and optimizing professional performance. The development of a culture of safe behavior in the information society involves a transformation of attitudes towards “information security” from the meaning of protecting information to ensuring the “security” of information for individuals and society.

Effects of digitalisation on preventive care and time pressure of primary care physicians

Abstract Background Excessive workload and waiting times in primary care, along with problems in information flow, can hamper the achievement of the public health benefits of preventive care. Digitalisation aims to promote prevention by expediting access to care and advancing self-care and seamless care, which might also streamline work. This study examined whether perceived changes in patient care due to digitalisation (teleconsultations, patient activity, and interprofessional collaboration) were associated with perceived a) improvement in preventive care and b) reduction in time pressure among primary care physicians. Methods We used national survey data on Finnish primary care physicians (N = 1538) from spring 2021. We conducted multivariable logistic and linear regression analyses, adjusted for gender, sector, and technology-related stress. We present odds ratios (OR), unstandardised b values, and their confidence intervals (CI) for statistical significance. Results Physicians who reported performing many teleconsultations (OR = 1.56, 95% CI 1.16-2.12) and perceived improvements in patient activity (OR = 5.02, 95% CI 3.73-6.75) or interprofessional collaboration (OR = 4.33, 95% CI 3.31-5.66) had greater odds of perceiving enhanced preventive care compared to their counterparts. Many teleconsultations (b=.26, 95% CI .14-.37) and perceived improvements in patient activity (b=.11, 95% CI .01-.21) or interprofessional collaboration (b=.12, 95% CI .02-.23) were also associated with higher time pressure (scale 1-5) among physicians. Conclusions Physicians may perceive that digitalisation improves preventive care when teleconsultations are frequently used, patients are encouraged to digital self-care, and practices for interprofessional collaboration are developed. Despite the patient benefits, the new tasks seem to increase physicians’ time pressure. Allocation of physicians’ working time to digital encounters and training of new tasks could help improve working conditions in primary care. Key messages • Primary care physicians who frequently utilise teleconsultations and see progress in patient activity or interprofessional collaboration due to digitalisation may find improvements in preventive care. • Digital tasks that promote preventive care may increase physicians’ constant rush and pressure to perform their duties, which should be recognised in work planning to ensure high-quality patient care.

Improving Brain Tumor Classification with Deep Learning Using燬ynthetic燚ata

Deep learning (DL) techniques, which do not need complex pre-processing and feature analysis, are used in many areas of medicine and achieve promising results. On the other hand, in medical studies, a limited dataset decreases the abstraction ability of the DL model. In this context, we aimed to produce synthetic brain images including three tumor types (glioma, meningioma, and pituitary), unlike traditional data augmentation methods, and classify them with DL. This study proposes a tumor classification model consisting of a Dense Convolutional Network (DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between layers. By comparing models trained on two different datasets, we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network (CycleGAN) on the generalization of DL. One model is trained only on the original dataset, while the other is trained on the combined dataset of synthetic and original images. Synthetic data generated by CycleGAN improved the best accuracy values for glioma, meningioma, and pituitary tumor classes from 0.9633, 0.9569, and 0.9904 to 0.9968, 0.9920, and 0.9952, respectively. The developed model using synthetic data obtained a higher accuracy value than the related studies in the literature. Additionally, except for pixel-level and affine transform data augmentation, synthetic data has been generated in the figshare brain dataset for the first time.

SiSPRNet: end-to-end learning for single-shot phase retrieval.

With the success of deep learning methods in many image processing tasks, deep learning approaches have also been introduced to the phase retrieval problem recently. These approaches are different from the traditional iterative optimization methods in that they usually require only one intensity measurement and can reconstruct phase images in real-time. However, because of tremendous domain discrepancy, the quality of the reconstructed images given by these approaches still has much room to improve to meet the general application requirements. In this paper, we design a novel deep neural network structure named SiSPRNet for phase retrieval based on a single Fourier intensity measurement. To effectively utilize the spectral information of the measurements, we propose a new feature extraction unit using the Multi-Layer Perceptron (MLP) as the front end. It allows all pixels of the input intensity image to be considered together for exploring their global representation. The size of the MLP is carefully designed to facilitate the extraction of the representative features while reducing noises and outliers. A dropout layer is also equipped to mitigate the possible overfitting problem in training the MLP. To promote the global correlation in the reconstructed images, a self-attention mechanism is introduced to the Up-sampling and Reconstruction (UR) blocks of the proposed SiSPRNet. These UR blocks are inserted into a residual learning structure to prevent the weak information flow and vanishing gradient problems due to their complex layer structure. Extensive evaluations of the proposed model are performed using different testing datasets of phase-only images and images with linearly related magnitude and phase. Experiments were conducted on an optical experimentation platform (with defocusing to reduce the saturation problem) to understand the performance of different deep learning methods when working in a practical environment. The results demonstrate that the proposed approach consistently outperforms other deep learning methods in single-shot maskless phase retrieval. The source codes of the proposed method have been released in Github [see references].

Privacy Protection Method for Sensitive Weighted Edges in Social Networks

Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

Ideology and influence in the debate over Russian election interference

The salience of the debate over Russian political interference in the USA has increased significantly during the Trump administration. However, there is no consensus over how to respond to the interference of this openly illiberal power. This paper argues that we need to reconceptualize our understanding of Russian influence to understand this high level of contestation. While the current understanding of Russian influence is characterized as a problem of unwanted information flows, we argue that we also need to take seriously the ideological influence that the Russia regime has in the USA—that the appeal of the Russian regime’s conservative and populist ideology can help to explain this division. By taking ideological attraction seriously, we can explain why it is currently difficult for the USA to respond to this threat and why solutions to Russian interference based on the information flow models need to be reconsidered.

Internet of Things Enabled Intelligent Energy Management and Control System for Heavy Equipment Industrial Park and Fuzzy Assessment of Its Schemes

In order to solve the problems of poor information flow, low energy utilization rate and energy consumption data reuse in the heavy equipment industrial park, the Internet of Things (IoT) technology is applied to construct the intelligent energy management and control system (IEMCS). The application architecture and function module planning are analyzed and designed. Furthermore, the IEMCS scheme is not unique due to the fuzziness of customer demand and the understanding deviation of designer to customer demand in the design stage. Scheme assessment is of great significance for the normal subsequent implementation of the system. A fuzzy assessment method for IEMCS scheme alternatives is proposed to achieve scheme selection. Fuzzy group decision using triangular fuzzy number to express the vague assessment of experts is adopted to determine the index value. TOPSIS is modified by replacing Euclidean distance with contact vector distance in IEMCS scheme alternative assessment. An experiment with eight IEMCS scheme alternatives in a heavy equipment industrial park is given for the validation. The experiment result shows that eight IEMCS scheme alternatives can be assessed. Through the comparisons with other methods, the reliability of the results obtained by the proposed method is discussed.

Identifying multiple stakeholders’ roles and network in urban waste separation management-a case study in Xiamen, China

Urban waste management is a complex social-ecological system that requires broader stakeholder participation and widespread environmental awareness. However, further investigation is required to identify interdependencies among different stakeholders and improve their interactions within the system. This research took Xiamen, a Chinese city, as a case study, which was one of 8 Chinese pilot cities that implemented waste separation in 2000 and one of the 46 key cities participating in a new separation policy since 2017. We used stakeholder analysis (SA), social network analysis (SNA) and ecological network analysis (ENA) to identify the characteristics of different stakeholders, their network structure, and the beneficiaries and contributors to the urban waste separation management (UWSM) system. The research conducted a comparison of resident’s participation before and after the new waste separation policy, as well as a comparison among different stakeholders in the new policy. The results showed that all stakeholders had a high level of satisfaction in 2017, but that satisfaction levels decreased with distance from the downtown area. Residents were more willing to participate in waste separation in 2017 than in 2012. Based on their powers and interests, stakeholders were divided into 4 categories that had different network centrality. The ecological relationships between stakeholders were mainly exploitative and competitive which may cause potential inefficiency; powerful stakeholders did not benefit the whole system and the least powerful stakeholder was the largest contributor. The combined application of these methods were complementary to help diagnose information flow problems in the socio-ecological system. Policy implications to further promote mutualism in UWSM are discussed.

A Systems Perspective on the Environmental Prediction Enterprise

AbstractOur success with environmental prediction could be considered among humankind’s most remarkable developments over the past 50 years. It protects lives and property, and helps us advance the well-being of society. Vast changes have occurred recently to the complexity and scope of our weather enterprise. Given its importance to society, there is reason to optimize our approaches for advancing its scope and capabilities. This essay highlights three points that may help facilitate this optimization, with an overarching suggestion to more overtly embrace a systems (engineering) approach. 1) Continued emphasis should be placed on advancing Earth system science as the foundational knowledge that advances weather prediction as well as the more holistic scope of environmental prediction (EP). 2) The complexity and coupling of the social, programmatic, observation, modeling, analytic, and interdisciplinary landscapes within the EP enterprise suggest adding a system engineering perspective/approach to further optimize outcomes and limit vulnerabilities. 3) The consideration of the enterprise as a data to information flow problem highlights opportunities and focal points to leverage that could help to advance the societal benefits derived from the EP enterprise. A generalized, highly simplified systems perspective on the advancement of Earth science and environmental prediction is offered by framing a simple equation involving the synthesis of observations, models, and programmatics that in turn yield science and applications benefits. Simplifications and derivatives of this equation are used to distill challenges and opportunities for further advancing enterprise benefits and to motivate considerations of scope and priorities related to our community’s decadal survey(s).

Struggles with infrastructures of information concerning hospital-to-home transitions.

Homecare nurses play a unique role in providing care during the follow-up after hospital discharge and in preventing readmission. The aim of this study was to explore the key challenges faced by homecare nurses in relation to caring for discharged patients. Data were collected through five focus group interviews with 29 Danish homecare nurses and subjected to inductive content analyses. The key challenges faced by homecare nurses fell into three themes: struggling to see the bigger picture, caring for patients from a distance, and compromising on professionalism. The findings demonstrated a paradox between the need for information and the struggle to access this information due to complicated infrastructures of information-sharing. Homecare nurses took on a substantial responsibility in providing the best possible care despite having limited information. Ironically, by taking on this responsibility, they implicitly contribute to covering up the problems of organisational and professional information flow.

A Global Optimal Approach to Wireless Space Information Flow

In wireless networks, inter-session and intra-session network coding can potentially reduce the energy consumption for information transmission. However, the optimal routing and channel selection render the problem non-convex and NP-hard. Given the coordinates of terminal nodes and the transmission tasks, we formulate the problem as a cost minimization problem, which we refer to as the wireless space information flow (WSIF) problem. We then employ a filled function method to compute the global optimal solution to the WSIF problem. The filled function method enables simultaneous optimization of the routing scheme, as well as the coordinates of the relay nodes.

On Points Focusing Entropy

In the paper, we consider local aspects of the entropy of nonautonomous dynamical systems. For this purpose, we introduce the notion of a (asymptotical) focal entropy point. The notion of entropy appeared as a result of practical needs concerning thermodynamics and the problem of information flow, and it is connected with the complexity of a system. The definition adopted in the paper specifies the notions that express the complexity of a system around certain points (the complexity of the system is the same as its complexity around these points), and moreover, the complexity of a system around such points does not depend on the behavior of the system in other parts of its domain. Any periodic system “acting” in the closed unit interval has an asymptotical focal entropy point, which justifies wide interest in these issues. In the paper, we examine the problems of the distortions of a system and the approximation of an autonomous system by a nonautonomous one, in the context of having a (asymptotical) focal entropy point. It is shown that even a slight modification of a system may lead to the arising of the respective focal entropy points.

The Information Flow Problem in multi-agent systems

One of the problems related to the multi-agent systems area is the adequate exchange of information within the system. This problem is not only related to the availability of highly efficient and sophisticated message-passing mechanisms, which are in fact provided with by current multi-agent platforms, but also to the election of an appropriate communication strategy, which may also greatly influence the ability of the system to cope with the exchange of large amounts of data. Ideally, the communication strategy should be compatible with how the information flows in the system, that is, how agents share their knowledge with each other in order to fulfill the system-level goals. In this way, MAS designers must deal with the problem of analyzing the multi-agent system with respect the communication strategy that best suits the way the information flows in that particular system. This paper presents a formalization of this problem, which has been coined as the Information Flow Problem, and also presents a complete case study with an empirical evaluation involving four well-known communication strategies and eight typical multi-agent systems.

Problems of Automation and Management Principles Information Flow in Manufacturing

Automated control systems of technological processes are complex systems that are characterized by the presence of elements of the overall focus, the systemic nature of the implemented algorithms for the exchange and processing of information, as well as a large number of functional subsystems. The article gives examples of automatic control systems and automated control systems of technological processes held parallel between them by identifying strengths and weaknesses. Other proposed non-standard control system of technological process.

Strategy for Developing Cold Chain Logistics in Zhengzhou Air Harbor Comprehensive Economic Experimentation Zone Based on PEST Analysis

Regional economic cooperation has become a common economic phenomenon in various parts of the world under the background of economic globalization. Logistics, information flow, capital flow and other circulation problems continually emerged. Logistics activities is becoming more important as a sign of economic development. There are less researchers in regional cold chain logistics in China, which is incompatible with the development of cold chain logistics proposed in 12th five-year plan. If the theoretical research lacked, it will restrict the development of cold chain logistics. In this paper, the research is based on the actual situation of Zhengzhou, and it uses the PEST process to analyze the environment and politics. It accordingly proposed the corresponding strategy and suggestions.

Modeling Patient-Centered Cognitive Work for High-Value Care Goals

Patient-centered cognitive support has been shown to be critically important to facilitate the effective use of health information technologies (HIT). There is a well-documented need to better understand HIT-mediated clinical workflow. Current technologies can burden clinicians’ cognitive resources, which is associated with patient safety risks and medical errors. We sought to employ a distributed cognition approach to examine how information flows across the activity system to support clinicians’ problem-solving. Specifically, we studied the propagation of representational states across media, conversations, actors and time in the coordination of patient-care processes. We examined multiple instances of work and information flow in a real-world setting, revealing problems in information flow: a) use of paper artifacts has limitations to facilitating coordination of care, b) clinicians challenged in developing shared awareness, c) responsibility of representing patient states is distributed across documents, d) clinical reasoning that informed care plans was absent from documents. Findings surface a challenge to automated monitoring of care goals; much of the information is present only in clinicians’ minds and in informal documents.

Sustaining Smallholder Farmers’ Livelihoods through Rainfall-Deficit-Index-Based Crop Insurance in Drought-Prone Areas: Lessons from Ethiopia

The main objective of this article is to assess the contributions in sustaining the livelihoods of smallholder farmers of rainfall-deficit-index-based crop insurance pilot project based on haricot beans implemented in Ethiopia in 2009. Based on cross-sectional data, assessment results revealed that crop insurance has a potential in sustaining the livelihoods of smallholder farmers, but that there was a problem of information flow, belated payout and inadequate consideration of farmers’ preferences. Scaling-up crop insurance scheme in the future increasingly depends upon building farmers’ knowledge on how the scheme works, through proper agricultural extension services and farmers’ active participation. Signifying crop insurance as an appropriate risk management strategy, the article underlines the importance of public extension services besides appreciating farmers’ motivations to take over the management and leadership of their own affairs when existing structures are not responsive in the context of practicing crop insurance.

On the Computational Complexity of the Linear Solvability of Information Flow Problems with Hierarchy Constraint

On the Computational Complexity of the Linear Solvability of Information Flow Problems with Hierarchy Constraint

An information theoretic approach to network tomography

In this article, we formulate an information theoretic approach to information recovery for a network flow transportation problem as an ill-posed inverse problem and use nonparametric information theoretic methods to recover the unknown adaptive-intelligent behaviour traffic flows. We indicate how, in general, information theoretic methods may provide a solution to the ill-posed inverse information flow problems, when a function must be inferred from insufficient sample information. As an application, we examine a data set which comprised traffic volumes at Bell Labs.