Efficiency Of Information Flow Research Articles

Dual-scale shifted window attention network for medical image segmentation

Swin Transformer is an important work among all the attempts to reduce the computational complexity of Transformers while maintaining its excellent performance in computer vision. Window-based patch self-attention can use the local connectivity of the image features, and the shifted window-based patch self-attention enables the communication of information between different patches in the entire image scope. Through in-depth research on the effects of different sizes of shifted windows on the patch information communication efficiency, this article proposes a Dual-Scale Transformer with double-sized shifted window attention method. The proposed method surpasses CNN-based methods such as U-Net, AttenU-Net, ResU-Net, CE-Net by a considerable margin (Approximately 3% ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 6% increase), and outperforms the Transformer based models single-scale Swin Transformer(SwinT)(Approximately 1% increase), on the datasets of the Kvasir-SEG, ISIC2017, MICCAI EndoVisSub-Instrument and CadVesSet. The experimental results verify that the proposed dual scale shifted window attention benefits the communication of patch information and can enhance the segmentation results to state of the art. We also implement an ablation study on the effect of the shifted window size on the information flow efficiency and verify that the dual-scale shifted window attention is the optimized network design. Our study highlights the significant impact of network structure design on visual performance, providing valuable insights for the design of networks based on Transformer architectures.

An efficient multi‐scale transformer for satellite image dehazing

AbstractGiven the impressive achievement of convolutional neural networks (CNNs) in grasping image priors from extensive datasets, they have been widely utilized for tasks related to image restoration. Recently, there is been significant progress in another category of neural architectures—Transformers. These models have demonstrated remarkable performance in natural language tasks and higher‐level vision applications. Despite their ability to address some of CNNs limitations, such as restricted receptive fields and adaptability issues, Transformer models often face difficulties when processing images with a high level of detail. This is because the complexity of the computations required increases significantly with the image's spatial resolution. As a result, their application to most high‐resolution image restoration tasks becomes impractical. In our research, we introduce a novel Transformer model, named DehFormer, by implementing specific design modifications in its fundamental components, for example, the multi‐head attention and feed‐forward network. Specifically, the proposed architecture consists of the three modules, that is, (a) multi‐scale feature aggregation network (MSFAN), (b) the gated‐Dconv feed‐forward network (GFFN), (c) and the multi‐Dconv head transposed attention (MDHTA). For the MDHTA module, our objective is to scrutinize the mechanics of scaled dot‐product attention through the utilization of per‐element product operations, thereby bypassing the need for matrix multiplications and operating directly in the frequency domain for enhanced efficiency. For the GFFN module, which enables only the relevant and valuable information to advance through the network hierarchy, thereby enhancing the efficiency of information flow within the model. Extensive experiments are conducted on the SateHazelk, RS‐Haze, and RSID datasets, resulting in performance that significantly exceeds that of existing methods.

Geographic Proximity And Firm Innovation: Evidence From The Chinese Manufacturing Industry

Innovation, competitive advantage, and firm performance are all positively affected by knowledge-sharing techniques. We consider the geographic proximity to research institutes as a proxy of the efficiency of knowledge sharing. We examine how and to what degree the practice of knowledge-sharing influences businesses' innovation using a sample of Chinese manufacturing companies chosen between 2009 and 2021. Thus, the relationship between corporate innovation and closeness to research institutions is investigated experimentally in this study. The decisive findings indicate that a company's ability to innovate is strongly impacted by its proximity to research institutions, which suggests that proximity may also lower coordination costs and enhance information flow efficiency. Additionally, this research reveals that institutions with a STEM (science, technology, engineering, and mathematics) concentration have a stronger correlation with information sharing and entrepreneurial innovation in the manufacturing sector. Furthermore, under similar conditions, the positive effect for non-SOEs and firms with lower R&D expenditures is more critical for firms close to research institutions. We find consistent evidence for a battery of robustness checks. Overall, the study provides insightful implications that university and research center clustering is advantageous for generating and exchanging necessary knowledge.

Research on the Continuous Participation of Virtual Brand Community in Decision-Making Based on Social Influence Theory

Based on the social influence theory, the influence of virtual brand community members’ perceived value on social influencing factors is discussed, and the influence of social influencing factors on virtual brand community members’ continuous participation in decision-making is analyzed. Through an empirical analysis of the survey data of Xiaomi users in the Xiaomi Community, the results showed that the perceived value of virtual brand community members significantly and positively affects social influencing factors, which also significantly and positively affect the members’ continuous participation in virtual brand communities. Therefore, only by sharing valuable information resources and improving the efficiency of information flow, thereby enhancing the perceived value of the community and increasing the stickiness of members to the virtual community, will we have an opportunity to enhance the interaction among members and effectively promote continuous participation in community activities through the strong bonds formed among members.

Implementation of a Hybrid Intelligence System Enabling the Effectiveness Assessment of Interaction Channels Use in HMI.

The article presents a novel idea of Interaction Quality Sensor (IQS), introduced in the complete solution of Hybrid INTelligence (HINT) architecture for intelligent control systems. The proposed system is designed to use and prioritize multiple information channels (speech, images, videos) in order to optimize the information flow efficiency of interaction in HMI systems. The proposed architecture is implemented and validated in a real-world application of training unskilled workers-new employees (with lower competencies and/or a language barrier). With the help of the HINT system, the man-machine communication information channels are deliberately chosen based on IQS readouts to enable an untrained, inexperienced, foreign employee candidate to become a good worker, while not requiring the presence of either an interpreter or an expert during training. The proposed implementation is in line with the labor market trend, which displays significant fluctuations. The HINT system is designed to activate human resources and support organizations/enterprises in the effective assimilation of employees to the tasks performed on the production assembly line. The market need of solving this noticeable problem was caused by a large migration of employees within (and between) enterprises. The research results presented in the work show significant benefits of the methods used, while supporting multilingualism and optimizing the preselection of information channels.

An Evolutionary Attention-Based Network for Medical Image Classification.

Deep learning has become a primary choice in medical image analysis due to its powerful representation capability. However, most existing deep learning models designed for medical image classification can only perform well on a specific disease. The performance drops dramatically when it comes to other diseases. Generalizability remains a challenging problem. In this paper, we propose an evolutionary attention-based network (EDCA-Net), which is an effective and robust network for medical image classification tasks. To extract task-related features from a given medical dataset, we first propose the densely connected attentional network (DCA-Net) where feature maps are automatically channel-wise weighted, and the dense connectivity pattern is introduced to improve the efficiency of information flow. To improve the model capability and generalizability, we introduce two types of evolution: intra- and inter-evolution. The intra-evolution optimizes the weights of DCA-Net, while the inter-evolution allows two instances of DCA-Net to exchange training experience during training. The evolutionary DCA-Net is referred to as EDCA-Net. The EDCA-Net is evaluated on four publicly accessible medical datasets of different diseases. Experiments showed that the EDCA-Net outperforms the state-of-the-art methods on three datasets and achieves comparable performance on the last dataset, demonstrating good generalizability for medical image classification.

ЦИФРОВА ТРАНСФОРМАЦІЯ ІНФРАСТРУКТУРИ МОРСЬКИХ ПОРТОВИХ КЛАСТЕРІВ

The article highlights the potential of digital transformation in optimizing maritime port clusters. A primary focus is given to the implementation of blockchain technologies to ensure transparency, security, and efficiency of information flows. Digital transformation is highly significant for seaport clusters, as it enhances the efficiency and competitiveness of these facilities. The authors examine the need for business process and management strategy adaptation to maximize the benefits of technological upgrades. Port clusters must learn to swiftly respond to changing market conditions, leveraging digital innovations to maintain competitiveness and steady growth. The integration of "green" technologies also appears promising for the sustainable development of the port industry. The article discusses the role of a specialized portal that facilitates coordination and integration of port activity participants, improving service quality and optimizing responses to market challenges. Based on research conducted, the authors have constructed a diagram outlining the functional capabilities of the portal for a seaport cluster. This diagram serves to identify the key functional components of the portal, and to improve comprehension and optimise processes taking place within the seaport cluster. The portal facilitates centralised control and management, enhancing seaport cluster productivity, safety and competitiveness. The authors analysed the platform's integrated capabilities to optimise seaport cluster activities. These functionalities aim to enhance resource management, streamline logistics processes and promote collaboration among different members of the port cluster. This, in turn, leads to better productivity and rational resource utilization. The study's results demonstrated that implementing digital transformation via blockchain technology in seaport clusters using a portal enhances operation management and security. Additionally, it encourages resource optimization and industry sustainability, thereby rendering it more competitive and viable amidst present-day demands and challenges.

An Innovative Approach to PMI Analysis and Enhancing Information Flow Efficiency

An Innovative Approach to PMI Analysis and Enhancing Information Flow Efficiency

The Impact of Information Flow by Co-Shareholder Relationships on the Stock Returns: A Network Feature Perspective

One shareholder may invest in different listed energy companies, so the information held by common shareholders can be transmitted among companies. Based on the two-mode complex network method, we construct an information flow shareholder-based network and employ different network indicators representing features of information flow as variables to construct panel regression models to analyze the impact of information flow among listed energy companies on the stock returns. The results indicate that the information flow of listed energy companies are increasingly important and play a significant role over a period. The efficiency of information flow among listed energy companies is increasingly high and the network information is concentrated among a few of these companies. The efficiency of information flow and the independence of listed energy companies are significantly positively related to stock returns, while the listed energy companies’ ability to control information is not significantly related to stock returns. We employ a new perspective to analyze the information flow on how to influence stock returns, and offer some related suggestions for investors and policy makers in the future.

Communication and building positive relationships within project teams in non-governmental organizations

Purpose: The aim of the study was to understand the role of communication and building positive relations in the NGO project team and to answer the following questions: (1) what factors and behaviors influence positive relations in the NGO project team? and (2) to what extent direct and indirect communication affect the efficiency of information flow? Design/methodology/approach: The authors conducted a case study in a selected project team operating in a non-governmental organization in Poland. Findings: The results of the study allow one to better understand the specific challenges the selected NGO project team was facing. Thus, they add new knowledge to the issues on effective communication and building positive relationships in NGO project teams. Moreover, the case study outcomes set the guidelines for further in-depth studies of a quantitative nature. Research limitations/implications: Being aware of the limitations resulting from the deliberate selection of the project team for the study, an analysis of the answers obtained was undertaken, the results of which allow one to present the issues of communication and building positive relationships within project teams in non-governmental organizations. In order to make generalizations, further in-depth research in this area should be carried out. Practical implications: In non-governmental organizations, authorities and project managers should increase their competences in the field of effective communication and building positive relationships in project teams in order to successfully implement projects. Social implications: Knowledge of the methods of effective communication and building positive relationships in project teams allows to increase awareness among members of non- governmental organizations of how the ways of working and team involvement affect the success of projects. Originality/value: A case study was conducted in a selected project team operating in an NGO on the importance of communication and building positive relationships. The article is addressed to people involved in NGO project management research and to third-sector entities.

Attitude, knowledge and constraints associated with the use of mobile phone applications by farmers in North West Nigeria

The evolution of mobile phone applications has opened up a platform for easy and real time dissemination and exchange of agricultural information among agricultural extension officers, farmers, agricultural institutions and non-governmental institutions. This study examined attitude, knowledge and constraint associated with the use of mobile phone apps by farmers in North West region of Nigeria. A descriptive survey design was adopted; data collection tool was pre-tested and administered as interview schedule to randomly sampled farmers. Data were analyzed using descriptive statistics and correlation. The findings showed that farmers have positive attitude towards mobile phone applications, and knowledgeable of simple phone operation techniques. The constraints to the use of mobile phone applications are high cost of phones, poor network, poor power supply, high cost of airtime and complexity in operating phones. This study recommends training and re-retraining of farmers and extension agents on the use of mobile phone apps for effective information sharing among farmers in the region. • Mobile cell phones enhance the efficiency of information flow among smallholder farmers. • The utilization of the mobile phone apps is dependent on attitude, knowledge and constratints of farmers. • This study examined attitude, knowledge and constraints among smallholder farmers' utilization of mobile phone apps for information sharing. • Farmers in the study area use selected few of the the available mobile phone apps on agricultural information sharing.

Providing a Model for Evaluating the Efficiency of Information Flow in the Supply Chain Using Data Envelopment Analysis

Providing a Model for Evaluating the Efficiency of Information Flow in the Supply Chain Using Data Envelopment Analysis

A subset selection based approach to structural reducibility of complex networks

Most of the real world networks such as the internet network, collaboration networks, brain networks, citation networks, powerline and airline networks are very large and to study their structure and dynamics one often requires working with large connectivity (adjacency) matrices. However, it is almost always true that a few or sometimes most of the nodes and their connections are not very crucial for network functioning or that the network is robust to a failure of certain nodes and their connections to the rest of the network. In the present work, we aim to extract the size reduced representation of complex networks such that new representation has the most relevant network nodes and connections and retains its spectral properties. To achieve this, we use the Subset Selection (SS) procedure. The SS method, in general, is used to retrieve maximum information (based on Frobenius norm) from a matrix in terms of its most informative columns. The retrieved matrix, typically known as subset has columns of an original matrix that have the least linear dependency. We present the application of SS procedure to many adjacency matrices of real-world networks and model network types to extract their subset. The subset owing to its small size can play a crucial role in analyzing spectral properties of large complex networks where space and time complexity of analyzing full adjacency matrices are too expensive. The adjacency matrix constructed from the obtained subset has a smaller size and represents the most important network structure. We observed that the subset network which is almost half the size of the original network has better information flow efficiency than the original network. Also, we found that the contribution to the Inverse Participation ratio of the network comes almost entirely from nodes that are there in the subset. This implies that the SS procedure can extract the top most influential nodes without the need for analyzing the full adjacency matrix.

(Un)intended consequences of networking on individual and network-level efficiency

Research shows that individuals benefit from setting up their personal networks strategically. There are two fundamental networking approaches: structural change, which involves the addition and deletion of communication ties, and frequency change, which means an increase or decrease of communication on existing ties. Existing literature has focused predominantly on structural change, leaving the potential of frequency change understudied. To investigate the differential effects of structural and frequency change on the efficiency of information flow through a network, we conduct simulations of networking approaches on a diverse set of real-world directed weighted communication networks. Our results indicate that, overall, frequency change is associated with higher efficiency than structural change in all but one of the studied cases. Structural change has a strong direct effect on individuals who actively modify their personal network. Surprisingly, our simulations also show that the indirect effects of frequency change (i.e., an individual’s ties are reshaped due to others’ actions) can exceed direct ones. Our results highlight the value of the so far less studied frequency change and suggest that using targeted network alterations can result in gains for information flow at the scale of the entire network.

Psychosocial stress reactivity is associated with decreased whole-brain network efficiency and increased amygdala centrality.

Cognitive and emotional functions are supported by the coordinated activity of a distributed network of brain regions. This coordinated activity may be disrupted by psychosocial stress, resulting in the dysfunction of cognitive and emotional processes. Graph theory is a mathematical approach to assess coordinated brain activity that can estimate the efficiency of information flow and determine the centrality of brain regions within a larger distributed neural network. However, limited research has applied graph-theory techniques to the study of stress. Advancing our understanding of the impact stress has on global brain networks may provide new insight into factors that influence individual differences in stress susceptibility. Therefore, the present study examined the brain connectivity of participants that completed the Montreal Imaging Stress Task (Goodman et al., 2016; Wheelock et al., 2016). Salivary cortisol, heart rate, skin conductance response, and self-reported stress served as indices of stress, and trait anxiety served as an index of participant's disposition toward negative affectivity. Psychosocial stress was associated with a decrease in the efficiency of the flow of information within the brain. Further, the centrality of brain regions that mediate emotion regulation processes (i.e., hippocampus, ventral prefrontal cortex, and cingulate cortex) decreased during stress exposure. Interestingly, individual differences in cortisol reactivity were negatively correlated with the efficiency of information flow within this network, whereas cortisol reactivity was positively correlated with the centrality of the amygdala within the network. These findings suggest that stress reduces the efficiency of information transfer and leaves the function of brain regions that regulate the stress response vulnerable to disruption. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Perancangan Sistem Informasi Pengendalian Kualitas pada Laboratorium Proses IV PT X

Effectiveness and efficiency can improve the performance of manufacturing companies. Such improvements can be achieved through the application of web-based applications. PT X is one of cement producers in Indonesia and to maintain its competitive advantage, PT X establishes several Process Laboratory responsible for its product quality control activities. One of them is Process Laboratory IV. Process Laboratory IV is still using spreadsheet based information system so that most of the activities and evaluation are still run manually. Some weaknesses are still encountered such as slow information flow rates, minimum levels of information security, and the potential for various forms of fraud. This research was conducted to design more efficient quality control information system through implementation of web based application. The information systems design created by building: (a) Business process diagrams (b) Use case diagrams, (c) Class diagrams, (d) Entity relationship diagrams, and (e) Databases, followed by web-based application design based on the results of information system design. The system design involves four actors with different authority and access levels to minimize information security issues. Through these conditions various forms of fraud can also be minimized. In addition, the system design also improves the information flow efficiency by eliminating the activity of re-input set point data. It can be concluded that the design of information systems with web-based applications have been able to help improve efficiency and minimize the weaknesses of current existing information systems in the PT X Process Laboratory IV.

The Application of Computer Systems Used in Logistics Centres by Courier Companies

In the era of pervasive computerization, the need for rapid and uninterrupted transmission and receiving information handling systems is an essential component of business operations. The use of information technology to use the full capabilities of systems supporting the implementation of the basic processes in the logistics centres ensures proper implementation of tasks. Hence, courier companies cooperating with logistic centres have the opportunity to use the information systems used in logistics centres for the execution of the courier business. The purpose of this article is to identify the impact of information systems used in logistics centres for courier companies services. The article presents a class of information systems used in logistics centres and the number of systems used by courier companies. Characterized the impact of information systems to improve the operation of logistics centres and assesses the extent to which the use of the information system of logistics centres affects the information flow in courier companies. The study showed that the use of the IT systems offered by the logistics centres streamlines the efficiency of information flow in the courier service. Research has shown which IT systems logistics centres are use and how their use by courier companies affects the information flow in courier services.

Distributed patterns of occipito-parietal functional connectivity predict the precision of visual working memory

Limitations in visual working memory (WM) quality (i.e., WM precision) may depend on perceptual and attentional limitations during stimulus encoding, thereby affecting WM capacity. WM encoding relies on the interaction between sensory processing systems and fronto-parietal ‘control’ regions, and differences in the quality of this interaction are a plausible source of individual differences in WM capacity. Accordingly, we hypothesized that the coupling between perceptual and attentional systems affects the quality of WM encoding. We combined fMRI connectivity analysis with behavioral modeling by fitting a variable precision and fixed capacity model to the performance data obtained while participants performed a visual delayed continuous response WM task. We quantified functional connectivity during WM encoding between occipital and parietal brain regions activated during both perception and WM encoding, as determined using a conjunction of two independent experiments. The multivariate pattern of voxel-wise inter-areal functional connectivity significantly predicted WM performance, most specifically the mean of WM precision but not the individual number of items that could be stored in memory. In particular, higher occipito-parietal connectivity was associated with higher behavioral mean precision. These results are consistent with a network perspective of WM capacity, suggesting that the efficiency of information flow between perceptual and attentional neural systems is a critical determinant of limitations in WM quality.

Development of a Cloud Based Remote Mobile Monitoring and Control System for Manufacturing

The purpose of this study is to develop a remote mobile monitoring and control system for manufacturing factory by using the cloud computing technology and information and communication (ICT) technology. In conjunction with the cloud and ICT technologies and mobile devices, modern manufacturing systems can be integrated to promote competitive strength to transmit the information and control commands toward the remote monitoring systems instantly via mobile devices. In such a way, this remote system could enhance the efficiency of information flow and the competitive strength and add unknown value to industries. This study adopts the cloud and ICT technologies as the main communication media to develop a remote monitoring and control system with smart mobile devices. The results of this study show the successful integration of the intelligent mobile devices and cloud networks for manufacturing system.

RFID-enabled indoor positioning method for a real-time manufacturing execution system using OS-ELM

Manufacturing execution systems (MES) have recently been introduced to monitor various manufacturing objects (MOs) in dynamic shop floors; they can leverage the efficiency of information flow across functional layers for planning and control. However, current MES practices using traditional indoor positioning algorithms face several difficulties in tracking MOs for wireless manufacturing: (1) inefficient wireless data acquisition in the shop-floor environment, (2) lack of a reliable and accurate real-time signal processing method for handling massive signal data, and (3) the positions of reference objects cannot be treated as in a static environment when unknown manufacturing orders arrive in a streaming manner. This paper proposes to handle the first challenge by adopting RFID technology that can constantly capture the wireless signals sent from tags mounted on various MOs. The second difficulty can be solved by applying the online sequential extreme learning machine (OS-ELM) that inherits the elegant properties of ELM in terms of extremely fast learning speed and high generalization performance. The OS-ELM based positioning method also addresses the third issue in which an online localization model has been constructed in a streaming manner. The proposed method can greatly reduce the training time without costly retraining of the previously trained data together with the newly arrived data. With the novel OS-ELM based RFID positioning framework, the MOs are upgraded to smart manufacturing objects (SMOs), and the processes are enhanced with real-time signal processing and intelligent tracking capabilities. The experimental results verify that the proposed positioning method is superior to other state-of-the-art algorithms in terms of accuracy, efficiency, and robustness.