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Abstract Background Adverse events occur frequently at emergency departments (ED) because of several risk factors related to varying conditions. It is still unclear, which factors lead to patient safety incident reports. The aim of this study was to explore the root causes behind ED-associated patient safety incidents reported by personnel, and based on the findings, to suggest learning objectives for improving patient safety. Methods The study material included incident reports (n = 340) which concerned the ED of a teaching hospital over one year. We used a mixed method combining quantitative descriptive statistics and qualitative research by inductive content analysis and deductive Ishikawa root cause analysis. Results Most (76.5%) incidents were reported after patient transfer from the ED. Nurses reported 70% of incidents and physicians 7.4%. Of the reports, 40% were related to information flow or management. Incidents were evaluated as no harm (29.4%), mild (46%), moderate (19.7%), and severe (1.2%) harm to the patient. The main consequences for the organization were reputation loss (44.1%) and extra work (38.9%). In the qualitative analysis, nine specific problem groups were found: insufficient introduction, adherence to guidelines and protocols, insufficient human resources, deficient professional skills, medication management deficiencies, incomplete information transfer from the ED, language proficiency, unprofessional behaviour, identification error, and patient-dependent problems. Six organizational themes were identified: medical staff orientation, onboarding and competence requirements; human resources; electronic medical records and information transfer; medication documentation system; interprofessional collaboration; resources for specific patient groups such as geriatric, mental health, and patients with substance abuse disorder. Entirely human factor-related themes could not be defined because their associations with system factors were complex and multifaceted. Individual and organizational learning objectives were addressed, such as adherence to the proper use of instructions and adequate onboarding. Conclusions System factors caused most of the patient safety incidents reported concerning ED. The introduction and training of ED -processes is elementary, as is multiprofessional collaboration. More research is needed about teamwork skills, patients with special needs and non-critical patients, and the reporting of severe incidents.

Information on Land Use and Land Cover Map (LULCM) is essential for environment and socioeconomic applications. Such maps are generally derived from Multispectral Remote Sensing Images (MRSI) via classification. The classification process can be described as information flow from images to maps through a trained classifier. Characterizing the information flow is essential for understanding the classification mechanism, providing solutions that address such theoretical issues as “what is the maximum number of classes that can be classified from a given MRSI?” and “how much information gain can be obtained?” Consequently, two interesting questions naturally arise, i.e. (i) How can we characterize the information flow? and (ii) What is the mathematical form of the information flow? To answer these two questions, this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM. This hypothesis is then supported by kinetic-theory-based experiments. Thereafter, upon such an entropy, a generalized Jarzynski equation is formulated to mathematically model the information flow, which contains such parameters as thermodynamic entropy of MRSI, thermodynamic entropy of LULCM, weighted F1-score (classification accuracy), and total number of classes. This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers. This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification, opening a new door for constructing domain knowledge.

In recent years, the growing incidence of blended and online learning has highlighted instructional design concerns, especially STEM instructional design. Existing studies have often adopted observations, questionnaires, or interviews to evaluate STEM instructional design plans. However, there is still a lack of quantitative, measurable, and objective assessment methods. To close this research gap, this study proposes an innovative method for assessing STEM instructional design plans based on information flows. In this study, 20 STEM instructional design plans were designed, analysed, and assessed using the proposed information flow-based assessment method. The results indicated that the proposed method is feasible and effective for assessing STEM instructional design plans. STEM instructional design plans could be significantly improved based on the proposed method. The results and implications for instructors and practitioners are discussed in depth.

This study analyzes the impact of the information environment (IE) and credit default swap (CDS) transaction costs on information transmission between the stock and CDS markets. Using the daily regression analysis on the Korean firm’s stock and CDS data from 2004 to 2023, the results show that companies with superior IE in the stock market exhibit a larger and more sensitive total information flow from the stock market to the CDS market. Companies with lower transaction costs in the CDS market demonstrate faster information flow. In the case of companies with superior IE, fundamental information is reflected in stock prices with high weight and thus the CDS spreads change reflecting information about stock prices. According to this study’s findings, the primary factor influencing the information flow from the stock market to the CDS market is the information environment of the company in the stock market, rather than transaction costs in the CDS market.

This paper seeks to analyze the information flow between the Bourse Régionale des Valeurs Mobilières (BRVM) and Environmental, Social, and Governance (ESG) stocks, focusing on the time and frequency domains. By studying these aspects, we aim to gain a deeper understanding of how information is transmitted between BRVM and ESG stocks, shedding light on the dynamics and interactions within this context. The study analyzes the decomposed daily returns of four indices: BRVM Composite Index (BRVMCI), BRVM 10 Index (BRVM10), FTSE/JSE SA All Share Index (FTSEJSE), and FTSE/JSE Top 30 Responsible Investment Index (FTSERI). We employed Rényi transfer entropy estimates to measure the information flow between the stocks returns.To ensure the robustness of the findings, the study also utilizes the Dynamic Conditional Co-variance-Generalized autoregressive Conditional Heteroscedasticity (DCC-GARCH) method. The study covers the period from June 23, 2014, to April 22, 2022. The results showed positive information flow but a mixture of significant and insignificant transfer entropies. The decomposed findings give evidence to reject the null hypothesis of no information flow in the short-and medium-terms but not in the longterm.The exchange of seemingly insignificant information among stocks presents opportunities for fund managers to diversify their portfolios across various time frames.To expedite economic growth and lower equity costs, institutional leaders should collaborate with governments to establish economic and political foundations that integrate markets. Such integration would yield benefits for both markets, but the BRVM stands to gain more due to its less developed and illiquid nature.

The identification of city communities is essential for the regional planning and management of metropolitan areas. City communities could be identified from the perspective of mobile flows, and integrating the advantages of multisource flow data is essential to measure intercity connectivity. In this research, a multisource flow fusion method, which avoids the one-sidedness of a single flow, is proposed to combine the characteristics of population flow, traffic flow, and information flow. Moreover, the silhouette coefficient and hierarchical clustering algorithm are used to determine the number of city communities and the final clustering result. The results show that (1) although population flow, cargo flow, and information flow are positively correlated, there are also some differences among them; (2) the fusion flow between cities in China presents an obvious diamond structure, among which Beijing, Shanghai, Guangzhou, and Chengdu are the four points of the diamond, and Wuhan is located almost in the centre of the diamond structure; and (3) city communities are identified based on multisource flow and hierarchical clustering algorithm, which meet the principles that closely connected cities are in the same community and nonclosely connected cities are in different communities.

With the increase of new energy penetration, the power balance and frequency stability problems brought by its randomness, intermittency and volatility has become increasingly serious. It is difficult to cope with these problems only by traditional centralized power plants. Flexible resources in the power systems should also bear a part of the responsibility for the balance of power and energy. Virtual power plant (VPP) can aggregate a large number of distributed flexible resources with different characteristics, participate in the electricity markets as a whole and accept the dispatch of the grid, and provide important support for the real-time power balance of the power systems. The development of VPPs should be based on a large number of flexible resources, advanced communication and dispatching/control technologies, and efficient business models and good market policies. The operation of VPPs can be attributed to the energy flow of the energy network, the information interaction of the information network and the value transfer of the value network. Therefore, based on the three-layer network architeciture of “energy-information-value”, the operation modes and control schemes of different types of VPPs were analyzed, and the idea of “three flow separation-convergence” for VPP architecture design was proposed. The findings provide useful guidance for the design, construction and operation of VPPs.

Adequate and desirable connections between suppliers and customers necessitate an appropriate flow of information. Therefore, a promising and proper data collaboration in the supply chain is of tremendous significance. Thus, the study’s main objective is to provide multiple objective programming models under uncertain conditions to assess the performance of suppliers. To meet that aim, a case study for the reliability assessment of the presented model is carried out. That section is associated with supply chain visibility (SCV). Likewise, the likelihood of unpredicted and undesirable incidents involving supply chain risk (SCR) is taken into consideration. The intimate relation between visibility and risk of the supply chain is deemed efficient for the performance of the supply chain. Incoherence in maximization and minimization of SCR and SCV and other factors, including costs, capacity, or demand, necessitates multiple objective programming models to assess suppliers’ performance to accomplish the before-mentioned aims. The study’s results indicate the high reliability of the proposed model. Besides, the numeral results reveal that decision-makers in selecting suppliers mainly decrease SCR and then attempt to enhance SCV. In conclusion, the provided model in the study can be a desirable model for analyzing and estimating supplier performance with SCR and SCV simultaneously.

Citrus fruit commodities cultivated by farmers have supply chain management problems at distribution agents and citrus fruit enthusiasts on a local scale. This study aimed to analyze the supply chain management (SCM) channel, the smoothness of SCM, and the efficiency of SCM in citrus fruits. The research was conducted in Poncokusumo Village, Malang Regency. Collecting data using interviews with respondents farmers and traders of oranges. Measurement of data using a Likert scale ranging from 1 to 5. The descriptive analysis method is used to describe the SCM phenomenon of citrus fruits, including information flow, commodity flow, and financial flow. The results showed 2 channel citrus fruits ended up in small traders. The smoothness analysis of SCM shows that the financial flow from small traders to middlemen is less smooth than the flow of commodities from small traders to large traders. The efficiency index and farmer share results indicate that channel two is efficient. It is recommended that middlemen apply a pre-order system so that packaging costs and transportation costs do not increase and wholesalers should communicate intensely with farmers so that sales information is conveyed directly to wholesalers.

This study examined Greece's Agricultural Knowledge and Innovation System (AKIS) and assessed the flow of information and linkages among eight stakeholder groups: policy, education, research, consulting, agricultural cooperatives, credit, private enterprises, and farmers. Data were collected using an online survey tool from 61 experts/representatives following an initial phone communication. The Graph Theoretical Technique was utilized to achieve the survey's objectives. The results revealed dominant and subordinated actors in the system and identified a critical pathway for information flow within AKIS. Policymakers can leverage these findings to strengthen linkages, address information gaps, and promote innovation and equitable development in the agricultural sector.

This study examined Greece's Agricultural Knowledge and Innovation System (AKIS) and assessed the flow of information and linkages among eight stakeholder groups: policy, education, research, consulting, agricultural cooperatives, credit, private enterprises, and farmers. Data were collected using an online survey tool from 61 experts/representatives following an initial phone communication. The Graph Theoretical Technique was utilized to achieve the survey's objectives. The results revealed dominant and subordinated actors in the system and identified a critical pathway for information flow within AKIS. Policymakers can leverage these findings to strengthen linkages, address information gaps, and promote innovation and equitable development in the agricultural sector.

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 directional organization of multiple nociceptive regions, particularly within obscure operculoinsular areas, underlying multidimensional pain processing remains elusive. This study aims to establish the fundamental organization between somatosensory and insular cortices in routing nociceptive information. By employing an integrated multimodal approach of high-field fMRI, intracranial electrophysiology, and transsynaptic viral tracing in rats, we observed a hierarchically organized connection of S1/S2 → posterior insula → anterior insula in routing nociceptive information. The directional nociceptive pathway determined by early fMRI responses was consistent with that examined by early evoked LFP, intrinsic effective connectivity, and anatomical projection, suggesting fMRI could provide a valuable facility to discern directional neural circuits in animals and humans non-invasively. Moreover, our knowledge of the nociceptive hierarchical organization of somatosensory and insular cortices and the interface role of the posterior insula may have implications for the development of targeted pain therapies.

The Internet of things (IoT) has recently received a great deal of attention, and there has been a large increase in the number of IoT devices owing to its significance in current communication networks. In addition, the validation of devices is an important concern and a major safety demand in IoT systems, as any faults in the authentication or identification procedure will lead to threatening attacks that cause the system to close. In this study, a new, three-phase authentication protocol in IoT is implemented. The initial phase concerns the user registration phase, in which encryption takes place with a hybrid Elliptic Curve Cryptography (ECC)–Advanced Encryption Standard (AES) model with an optimization strategy, whereby key generation is optimally accomplished via a Self-Improved Aquila Optimizer (SI-AO). The second and third phases include the login process and the authentication phase, in which information flow control-based authentication is conducted. Finally, decryption is achieved based on the hybrid ECC–AES model. The employed scheme’s improvement is established using various metrics.

This study explores fine-grained feature-based face attribute recognition techniques to enhance the accuracy of face recognition in low-resolution and complex environments. The article proposes a global feature extraction method and a local texture feature extraction method to extract global and regional features by enhancing feature reuse and information flow through dense connectivity and ShuffleNet V2 framework. Then, a multiscale feature exchange method is used to fuse different scale features to enhance the capture of detail information. Finally, efficient feature integration is achieved by the multiscale feature fusion method. Experimental results on the CK+ and FER2013 datasets show that the accuracy of this method on face expression recognition reaches 97.24% and 95.93%, respectively, and the average recognition accuracy in the face attribute recognition experiments on the CelebA dataset is 97.11%, which is significantly better than the comparison algorithm. In addition, the analysis of the recognition effect on low-resolution faces shows that this paper’s method achieves a recognition accuracy of 54.03% at a resolution of 15 × 15 and a high accuracy of over 99% at resolutions of 70 × 70 and above. These results show that the face attribute recognition technique based on fine-grained features proposed in this paper significantly improves recognition accuracy.

The transition towards smart grid introduces the potential for revolutionary changes in the present energy management systems. It provides the grid with the necessary functionalities to transform into a decentralized energy system, and integrate large-scale variable renewable energy sources with enhanced demand-side management. Saudi Arabia is among the countries with significant potential to generate electricity from renewable energy sources, especially solar. The development of smart energy, smart communication, and smart information systems to establish a state-of-the art smart grid that enables not only security and automation but also has the flexibility of integrating technological innovation in the upcoming years. The model developed is characterized as government-led, commercially-driven, based on technological readiness, and community-oriented with numerous projects systematically initiated targeting innovation, human resources development, and industrial viability. Therefore, this paper aims to highlight the trends associated while considering the case scenario of the country and outlining the challenges and potential solutions with the establishment of smart grids.

This paper investigates the fact that construction projects, due to their specificity, are complex, temporary and dynamic. Over their course, participants change, successive construction works are done and new information becomes available. This carries over to difficulties in communication. In the literature, numerous studies note the fact that a network-based approach to the analysis and monitoring of communication as a part of complex construction projects is commendable. Relations between agents, knowledge and tasks in the context of communication within a construction project can be visualized in the form of a meta-network, and suitably developed structural measures can be used to analyze them. In this paper, the authors used meta-network theory to analyze relations between project participants, knowledge and tasks in the context of communication within a construction project, on the basis of the construction of a housing estate located in Katowice, Poland. Meta-network structural analysis allowed for a deeper understanding of these relations and the detection of essential information about the level of communication in the project under investigation, which was a basis for further discussion. The authors also stress the benefits from the approach presented and argue that it should be a starting point for effective management in the sphere of communication in construction companies.

The information diffusion and interactions within financial markets have a significant impact on the price discovery process and the sentiment and risk dispersion. Despite its importance, limited research has been conducted on information flow dynamics within the Tehran Stock Exchange, which is a vital component of Iran's capital market. This study aims to fill this gap by examining the information flow dynamics among 39 major industries from March 27, 2010, to June 21, 2023. Effective transfer entropy is employed to quantify the intensity of information flow between industry indices. Sequence of information matrices are constructed using rolling one-week windows over one-year periods. Given the occurrence of critical events during the research period, their influence on information flow dynamics is analyzed using Frobenius distance-based k-nearest neighbor networks, Influence Strength analysis, and threshold networks. The findings reveal that the effective transfer entropy matrix exhibits time-varying characteristics and remains stable throughout most periods. Furthermore, critical events significantly impact information flow dynamics, with abnormal values of Influence Strength associated with market volatility and major events. Additionally, the dominant source of information in the information flow network changes over time, highlighting the transient nature of industry dominance within the network.IntroductionThe diffusion of information and interactions within financial markets greatly influences the price discovery process and affects sentiment and risk dispersion. The potential for growth in the Tehran Stock Exchange (TSE) through the introduction of innovative financial instruments can offer investors additional investment opportunities. Therefore, understanding the dynamics of information transmission within the market aids investors in decision-making.Existing literature suggests that stock price volatilities are interconnected, and stocks within the same industry often exhibit high correlations. Additionally, industry stock price indexes within the market can serve as leading indicators of economic activity. Analyzing the information flow network at the industry index level holds significant implications for investors, portfolio managers, and policymakers seeking to devise appropriate risk-mitigating strategies, especially industry sector rotation strategies.Despite the Tehran Stock Exchange being a vital component of Iran's capital market, there has been limited research on the information flow network between industries and its time-varying characteristics. Furthermore, despite significant events occurring during the specified sample period, there is a lack of empirical evidence regarding their impacts on information flow within the Tehran securities market.Methods and MaterialIn this research, the dynamics of information flow between the 39 major industries are investigated from March 27, 2010, to June 21, 2023. Following Ni (2023), the Effective transfer entropy that measures the intensity of information flow between industries indices is calculated. Then the sequence of information matrices is created by rolling a one-week calculation window. In this paper, the calculation window of 237-trading day widths and the rolling window of 5-day widths are used to calculate the information matrices of length 591. Moreover, using quantiles of return series, and , the information matrix sequences are constructed.Given that the research period encompasses critical events, their influence on information flow is examined using various methodologies, including the Frobenius distance-based k-nearest neighbor network, Influence Strength (IS) analysis, and a threshold-directed network of information matrices. Results and DiscussionUpon depicting the Frobenius distance matrix based on Q1, significant shifts in the distance between the information matrices are observed. These shifts often coincide with critical events that have impacted the market.The IS series graph over the research period reveals several local peaks. For some peaks, no significant events occurred during the research period. Peak 2, however, corresponds to severe market fluctuations and turmoil, primarily stemming from the global impact of the 2008 financial crisis. Additionally, this time window aligns with the initial period of oil and petrochemical sanctions against Iran, leading to a decline in the total index of the TSE. Peak 4 reflects a decrease in the TSE's total index following Iran's nuclear agreement with the P5+1 in 2015 (post-JCPOA). During peak period 5, coinciding with the US withdrawal from the JCPOA and the re-imposition of all US sanctions, the TSE's total index experienced a drop. Peaks 1, 3, and 7 correspond to the bursting of stock price bubbles in 2009, 2013, and 2020, respectively.The findings also highlight that the window corresponding to the maximum value of IS (0.1757) is from 31/12/2012 to 7/1/2014, coinciding with the bursting of the stock price bubble in January 2014. Peak 6 corresponds to the window from 19/7/2020 to 7/7/2021, which includes the early days of the COVID-19 pandemic. Lastly, from 1/6/2022 to 3/6/2023, the government's decision to abolish the preferential exchange rate for importing basic goods negatively affected the prices of some listed companies in the TSE and the indexes of related industries. Comparing the patterns of IS calculated based on Q1 vs. Q2 demonstrates the correspondence between the local peaks.On the other hand, examining the Financial industry (node 37), the series reached its peak during 2/2/2016-25/1/2017. During this period, the TSE faced a significant decline in the total index due to uncertainty caused by the JCPOA. Analysis of the directional network of the information matrix, filtered with a threshold of 0.01, reveals that in the post-JCPOA period, there is an information flow between the Financial industry and all other industries except the Furniture industry (node 20) and Peymankari industry (node 26).Furthermore, aside from node 37, which serves as the central node during this period, node 34 (Banking industry, deg = 34), node 39 (EstekrajeNaft industry, deg=33), and node 35 (SayerMali industry, deg =32) also exhibit high degrees. Additionally, the network constructed from the information matrix corresponding to peak 6 indicates several central nodes. However, during the time window corresponding to peak 6, node 24 (Daroee industry) with the highest (0.0105) exerts the strongest influence on the network.The results also demonstrate that for certain industries, such as the Pharmaceutical industry, the value of increased during the 19/1/2016-11/1/2017 period, corresponding to the post-JCPOA era. However, for other industries, the maximum value of occurred mainly during other critical periods, such as the stock price bubble bursts in 2010 and 2014 and the imposition of new sanctions against Iran..ConclusionThe findings indicate that the effective transfer entropy matrix exhibits time-varying characteristics and remains stable over the majority of periods. Additionally, critical events have notably impacted the dynamics of information flow, with abnormal values of Influence Strength correlating with market volatility and significant events. Moreover, the primary source of information in the sequence of the information flow network evolves over time, suggesting that the dominant industry in the network is not consistently sustainable.

In musculoskeletal systems, describing accurately the coupling direction and intensity between physiological electrical signals is crucial. The maximum information coefficient (MIC) can effectively quantify the coupling strength, especially for short time series. However, it cannot identify the direction of information transmission. This paper proposes an effective time-delayed back maximum information coefficient (TDBackMIC) analysis method by introducing a time delay parameter to measure the causal coupling. Firstly, the effectiveness of TDBackMIC is verified on simulations, and then it is applied to the analysis of functional cortical-muscular coupling and intermuscular coupling networks to explore the difference of coupling characteristics under different grip force intensities. Experimental results show that functional cortical-muscular coupling and intermuscular coupling are bidirectional. The average coupling strength of EEG $\rightarrow $ EMG and EMG $\rightarrow $ EEG in beta band is 0.86 ± 0.04 and 0.81 ± 0.05 at 10% maximum voluntary contraction (MVC) condition, 0.83 ± 0.05 and 0.76 ± 0.04 at 20% MVC, and 0.76 ± 0.03 and 0.73 ± 0.04 at 30% MVC. With the increase of grip strength, the strength of functional cortical-muscular coupling in beta frequency band decreases, the intermuscular coupling network exhibits enhanced connectivity, and the information exchange is closer. The results demonstrate that TDBackMIC can accurately judge the causal coupling relationship, and functional cortical-muscular coupling and intermuscular coupling network under different grip forces are different, which provides a certain theoretical basis for sports rehabilitation.

Security researchers utilize taint analyses to uncover suspicious behaviors in Android apps. Current static taint analyzers cannot handle ICC, reflection, and lifecycles dependably, increasing the result verification cost. On the other hand, current dynamic taint trackers accurately detect execution paths. However, they depend on specific Android versions and modified devices, reducing their usability and applicability. In addition, they require app exercise every time running the taint analysis. This paper presents a new dynamic taint tracker called T-Recs, tracking information flows by recording and reconstructing the app execution. First, before the taint analysis, the app’s runtime data are obtained by instrumenting logging code into the app’s bytecode and running the app to be independent of specific Android versions and devices. Then, T-Recs performs the taint analysis accurately with the logged data and separately from the app exercise. This paper is an extended version of our work published. Previously, T-Recs’ accuracy was mainly evaluated in privacy leak detection. The results show that T-Recs outperforms compared analyzers, which are FlowDroid (w/ and w/o IC3), Amandroid, DroidSafe, and TaintDroid (w/ and w/o IntelliDroid). This paper also involves DroidRA and IccTA. This paper shows that T-Recs detects ICC- and reflection-related leaks missed by FlowDroid in popular Google Play apps. The other static analyzers fail to analyze most of the apps. These experiments also indicate an advantage of T-Recs: its users can re-execute T-Recs’ taint analysis without re-exercising the app. T-Recs’ app-runtime overhead and parallel execution performance were also evaluated, and the results are acceptable.

This study analyzes the information flow between the palm oil, soybean oil, and crude oil futures markets and proposes ways to develop the vegetable oil market, especially the palm oil futures market. In contrast to the results of a Granger causality test, we provide strong evidence that interactive cause–effect relationships exist between each pair of the three oil futures markets. Yet, the emergence of the net information flow depends on the trading volume and liquidity (i) from the crude oil futures to the two vegetable oil futures markets and (ii) from the soybean oil futures to the palm oil futures market. Our findings support the fact that the palm oil futures market is still subject to tight investment constraints and, therefore, fails to play a leading role in information discovery regarding the crude oil and soybean oil futures markets. This suggests that policymakers could pursue a series of initiatives, such as introducing US dollar-based trading and extending trading hours, to further develop the palm oil futures market and reduce market friction.

The Chengdu–Chongqing urban agglomeration (CCUA), as the only national-level city cluster in southwestern China, serves as a strategic support for the Yangtze River Economic Belt and an important demonstration area for promoting new urbanization in the country. The study of the networked characteristics of the CCUA contributes to a systematic understanding of its spatial connectivity patterns, optimization of spatial structure and layout, and promotion of high-quality regional development. In this study, we constructed models for traffic flow, information flow, migration flow, and composite flow to calculate the strength of connections between cities and the total flow of various elements in the CCUA. ArcGIS spatial visualization tools were used to depict the spatial connectivity patterns of the element flows within the CCUA. Additionally, social network analysis methods, including network density, centrality, and cohesive subgroups analysis, were employed to reveal the spatial network structure characteristics of the CCUA. The findings are as follows: (1) The overall structure of the cities within the CCUA is relatively loose, with significant differences in connectivity strength. It exhibits a west-strong and east-weak pattern, with Chengdu-Chongqing, Chengdu-Deyang, Chengdu-Mianyang, and Chengdu-Meishan occupying the top tier, while Zigong and Ya'an have relatively weak connections with other cities. Chengdu and Chongqing have prominent positions in the CCUA, with Chengdu having a more prominent core position compared to Chongqing, resulting in an overall hierarchical distribution of “1 + 1+7 + 7”. (2) The network density of the element flows in the CCUA is relatively low, indicating a generally weak element connectivity. The centrality of cities other than Chengdu and Chongqing is at a moderate to lower level, suggesting a weak overall resource connectivity capacity in the CCUA. (3) Most cities tend to form cohesive subgroups based on geographic proximity, while the cohesive subgroup in Chongqing is still in its early stages of development due to administrative boundaries. The research results quantitatively depict the spatial network structure characteristics of the CCUA, providing theoretical references for its development.

(1) Background: A current trend observed in the logistics sector is the use of Industry 4.0 tools to improve and enhance the efficiency of cargo handling processes. One of the popular solutions is an augmented reality system that supports operators in everyday tasks. The article aims to present design assumptions for implementing an augmented reality system to support air cargo handling at the warehouse. (2) Methods: Research was carried out based on a five-stage analytical procedure, aiming to analyze the current state and identify the potential for implementing the AR system. The following methods were used to collect data: co-participant observations, process analysis, direct interviews, analysis of internal documentation, and applicable legal regulations. (3) Results: The conducted research allowed for identifying information flows accompanying cargo flows and developing a project to automate selected information flows. The obtained results made it possible to identify operations for which the AR system’s implementation will increase their effectiveness and efficiency. (4) Conclusions: The obtained results identified the need to develop a hybrid algorithm for arranging cargo in the warehouse and to build a system supporting self-verification of markings on air cargo.

Information systems in small manufacturing companies can have a big impact on increasing economy and productivity. Due to limited human and financial resources, these companies generally use cheaper commercial solutions or develop specialized information system solutions that are adapted to their needs. In order to properly solve the problem of introducing an information system, a thorough analysis of information flows in the company and corresponding business processes is necessary. The main subject and goal of the research in this work is related to the investigation of time losses due to the absence of an information system from the aspect of managing the flow of cutting tools, as well as the presentation of the foundations for its development in a small manufacturing company.

The article presents the results of an applied political research on the representation of the 2022 Kazakh events in the Russian segment of social media. The context of the study stems from the significant intersection of the Russian and Kazakh segments of social media and the mutual influence of political processes. Under the political mobilization of the mass protests in Kazakhstan, which turned into an attempted coup, the activities of Russian counterelites intensified in various regions of Russia. Using a hybrid research strategy combined with cognitive mapping and social media analysis the authors managed to identify dynamic, structural and substantive characteristics of the information representation of the Kazakh events in the Russian segment of the Internet. The authors concluded that the scale of the Kazakh protests allowed to draw the short-term interest of the Russian audience, mainly from the regions bordering Kazakhstan. The use of economic triggers (rising prices, inefficient social policy, etc.) led to the involvement of the adult audience in the discussions around the Kazakh events, while a significant part of youth and young adults was excluded from information flows around the events. The study revealed that the Russian opposition attempted to use manipulative “contamination” technology in order to extrapolate the political and economic discontent in Kazakhstan to the situation in Russia. At the same time, the greatest resonance in the Russian segment of users was caused directly by the participation of the Collective Security Treaty Organization (CSTO) in the settlement, and not by the social and economic triggers that led to the events.

The effects of temperature on interaction strengths are important for understanding and forecasting how global climate change impacts marine ecosystems; however, tracking and quantifying interactions of marine fish species are practically difficult especially under field conditions, and thus, how temperature influences their interaction strengths under field conditions remains poorly understood. We herein performed quantitative fish environmental DNA (eDNA) metabarcoding on 550 seawater samples that were collected twice a month from 11 coastal sites for 2 years in the Boso Peninsula, Japan, and analyzed eDNA monitoring data using nonlinear time series analytical tools. We detected fish–fish interactions as information flow between eDNA time series, reconstructed interaction networks for the top 50 frequently detected species, and quantified pairwise, fluctuating interaction strengths. Although there was a large variation, water temperature influenced fish–fish interaction strengths. The impact of water temperature on interspecific interaction strengths varied among fish species, suggesting that fish species identity influences the temperature effects on interactions. For example, interaction strengths that Halichoeres tenuispinis and Microcanthus strigatus received strongly increased with water temperature, while those of Engraulis japonicus and Girella punctata decreased with water temperature. An increase in water temperature induced by global climate change may change fish interactions in a complex way, which consequently influences marine community dynamics and stability. Our research demonstrates a practical research framework to study the effects of environmental variables on interaction strengths of marine communities in nature, which would contribute to understanding and predicting natural marine ecosystem dynamics.

With the increasing threat of pests, weeds and diseases to agriculture, the environment and our way of life, governments are becoming more reliant on general surveillance to fill biosecurity surveillance gaps. That is, engaging people from all walks of life in the monitoring and reporting of pests, weeds and diseases. It is often thought that instigating a general surveillance program simply requires the development of reporting tools and creating awareness of the program among intended target groups, but this is a simplistic view. This paper explores whether a broader approach identifies better ways to implement general surveillance programs to meet their goals and remain sustainable over the long-term, and therefore assist in program management. A systems thinking approach was used to identify the components within general surveillance programs, and their interactions. These components are the actors or people involved and their relationships; infrastructure like reporting tools and resources; formal and informal rules; and species or diseases of interest and their environment (included to account for the biophysical context). We explored what helped and hindered the design and implementation of general surveillance programs, drawing on international literature and the experiences of people in nine case studies from across Australia and New Zealand. Systems thinking helped to identify leverage points in the case studies, including feedback loops and information flow paths, and facilitated thought on how they can be used to improve programs. For example, sharing knowledge and experiences of actors from throughout the program being notifiers (i.e. the people who report pests, weeds and diseases), laboratory or herbarium staff, funding bodies, policy makers and others, built trust and ownership in the program and facilitated learning and self-organisation. This increased the capacity for innovation, adaptation, and continual improvement, leading to ongoing program benefits. Approaching program management using systems thinking helps structure program monitoring and evaluation and better target effort and resources to improve performance. However, this requires a dedicated program coordinator (or coordination team) with adequate resources to identify challenges and opportunities and adapt the program accordingly.

Based on the methods of Fourier decomposition, correlation analysis and Liang-Kleeman information flow, the interaction between intensity and rainfall of Typhoon Rammasun (1409) is studied using the best track data of Shanghai Typhoon Institute (STI) of China Meteorological Administration (CMA), Tropical Rainfall Measuring Mission (TRMM) satellite 3B42 rainfall estimation data of National Aeronautics and Space Administration (NASA) and ERA5 reanalysis data of European Centre for Medium-Range Weather Forecasts (ECMWF) with 0.25°×0.25° grids. The results show that the rainfall of Typhoon Rammasun (1409) has obvious characteristics of asymmetry, and it is mainly located in the west side of the typhoon center. The rainfall of Typhoon Rammasun (1409) increases significantly twice during the whole life cycle, which corresponds to the intensification period. The information flow analysis shows that the rainfall of Typhoon Rammasun (1409) is affected by the intensity of typhoon itself, whereas the rainfall can also feedback on the latter. Compared with the influence of typhoon intensity on rainfall, the information flow from rainfall to typhoon intensity decreases by nearly an order of magnitude, indicating that the typhoon intensity plays a dominant role in the interaction relationship. The possible mechanism by which the typhoon intensity affects rainfall are analyzed by diagnosing the water vapor and dynamic conditions respectively. In terms of water vapor conditions, the convergence area of vertical integral of moisture flux corresponds well to the rainfall. The intensification (reduction) of Typhoon Rammasun (1409) partly results in the enhanced convergence (divergence) of vertical integral of moisture flux in the southwest of the typhoon center, which brings more (less) rainfall in this region. In addition, the South China Sea and the Western Pacific water vapor transport channel have obvious response to the changes of Typhoon Rammasun (1409) intensity. In terms of dynamic conditions, the strong center of vertical helicity in the middle and lower layers is mainly located in the west side of the typhoon center, indicating the rainfall area. The intensification (reduction) of Typhoon Rammasun (1409) leads to the increase (decrease) of absolute vertical helicity on the west side of the typhoon center, thus promotes (inhibits) the development of upward movement to a certain extent, resulting in more (less) water vapor condensation and rainfall.

There is a relationship between social media platforms and emerging infectious diseases (EIDs) such as the COVID-19 pandemic. Social media platforms are used to manage information flow in every EID. Individuals use the platforms to source information, while health workers, organisations, and other stakeholders use the platforms to provide and manage information flow. This study employed a narrative literature review (NLR) to understand the role of social media platforms in managing the COVID-19 pandemic information flow. The paper deployed three research questions as seen in the research methodology. The paper found a clear relationship between social media and EIDs and the COVID-19 pandemic. The article further provides the ways to use platforms to manage information during the COVID-19 pandemic.

An inhomogeneous economic space, representing a multitude of interactions between geographically distributed economic agents, can be differentiated into a number of districts. The experience of economic regionalization has approximately 260 years. However, this experience is poorly used in the transition to the digital economy and the analysis of the digital economic space formed as a result of the digital interaction of economic agents. There is no generalization of research on the differentiation of such a space in world science. Therefore, the purpose of our research was to generalize the world experience of regionalization of the digital economic space in order to identify emerging approaches and clarify existing problems and possible prospects. To achieve this goal, it was necessary to solve the following tasks: to identify the world array of publications on the subject under consideration; to conduct a comparative analysis of the identified publications with their subsequent grouping into approaches; to assess the possibilities and limitations of each approach; to outline the prospects for further research. About forty journal articles on the regionalization of the digital economic space have been identified in eight bibliographic databases using the author’s semantic search algorithm. A comparative analysis of these articles led to the conclusion that all of them are grouped into six approaches according to the method of allocation of districts: autocorrelation, cluster, comparative geographical, streaming, agglomeration and platform. The ideas underlying each approach are presented, as well as the main advantages and disadvantages. Prospects for further research may be related to the solution of the identified problems. As a result of comparing the array of articles with the experience of economic regionalization, five promising areas have been identified that go beyond the existing approaches

The nematode worm Caenorhabditis elegans has a relatively simple neural system for analysis of information transmission from sensory organ to muscle fiber. Consequently, this study includes an example of a neural circuit from the nematode worm, and a procedure is shown for measuring its information optimality by use of a logic gate model. This approach is useful where the assumptions are applicable for a neural circuit, and also for choosing between competing mathematical hypotheses that explain the function of a neural circuit. In this latter case, the logic gate model can estimate computational complexity and distinguish which of the mathematical models require fewer computations. In addition, the concept of information optimality is generalized to other biological systems, along with an extended discussion of its role in genetic-based pathways of organisms.

Abstract Maritime supply chain (MarSC) stakeholders interact with third parties (e.g. freight forwarders, 3PLs, financial institutes, custom authorities) to facilitate the cargo flow and exchange of information, documents, or financials. Hence, MarSC stakeholders are increasingly interested in innovative technological solutions that vouch for the authenticity and/or the ownership of digital assets without the control of a central third party. Extended research is carried out to prove how applications based on the distributed ledger technology or blockchain address these requirements, yet limited research investigates their purchasing process and economic implications. This paper uses the phytosanitary certificate in an international supply chain flow as a case study where interaction between multiple stakeholders is fundamental and analyses the purchase scenarios of a blockchain-based tool. To do so, it uses a theoretical model that identifies and quantifies the costs and benefits incurred by MarSC stakeholders, formulates gain-sharing scenarios and presents the results of a sensitivity analysis to show the dependence between the data-use and the potential economic gains it generates. The results show that freight forwarders could share economic benefits with shippers or consignees to anticipate purchasing a blockchain-based tool.

Communication and information flowduring construction project execution is often discussed in the literature. Numerous scholars note the presence of problems with communication and information flow and highlight that these problems also affect construction project completion time and cost. The vast majority of studies on the impact of communication on construction project completion time and cost takes on a qualitative character and there is a lack of quantitative analyses of this subject. To address these deficiencies, the authors of this paper propose a quantitative approach to assessing communication between construction project participants in the aspect of its impact on said project’s completion time and cost. The authors used meta-network theory to model and analyse the problem, as it can fully depict the problem’s complexity. The method proposed allows for dynamic identification of key information flow paths between project participants, which determine its performance in an essential way. The proposed approach can support decision-makers in effective management of communication between a construction project’s participants, which has a positive carryover to achieving planned project goals. The method was tested on a real-world development project that featured the construction of a housing complex in Katowice, Poland.

This research identified and analyzed, by a systematic review of the literature, the relationship between information and values in supporting the decision making, in the corporate sustainability context. It was verified that the relationships between information and values, in the researched context, take place from three "macro processes": Information and Knowledge Management, Informational Strategy, and Dissemination of Information. These relationships range all the stages of the decision making, affecting the rationale applied by the decision-maker regarding the adherence of corporate sustainability practices. This suggests that the organizations may benefit themselves when developing information presentation strategies aligned, not only to the stakeholders’ values, but also in the way that these values engage with the information.

Predictive tracking demonstrates our ability to maintain a line of vision on moving objects even when they temporarily disappear. Models of smooth pursuit eye movements posit that our brain achieves this ability by directly streamlining motor programming from continuously updated sensory motion information. To test this hypothesis, we obtained sensory motion representation from multivariate electroencephalogram activity while human participants covertly tracked a temporarily occluded moving stimulus with their eyes remaining stationary at the fixation point. The sensory motion representation of the occluded target evolves to its maximum strength at the expected timing of reappearance, suggesting a timely modulation of the internal model of the visual target. We further characterize the spatiotemporal dynamics of the task-relevant motion information by computing the phase gradients of slow oscillations. We discovered a predominant posterior-to-anterior phase gradient immediately after stimulus occlusion; however, at the expected timing of reappearance, the axis reverses the gradient, becoming anterior-to-posterior. The behavioral bias of smooth pursuit eye movements, which is a signature of the predictive process of the pursuit, was correlated with the posterior division of the gradient. These results suggest that the sensory motion area modulated by the prediction signal is involved in updating motor programming.

One challenge in neuroscience is to understand how information flows between neurons in vivo to trigger specific behaviors. Granger causality (GC) has been proposed as a simple and effective measure for identifying dynamical interactions. At single-cell resolution however, GC analysis is rarely used compared to directionless correlation analysis. Here, we study the applicability of GC analysis for calcium imaging data in diverse contexts. We first show that despite underlying linearity assumptions, GC analysis successfully retrieves non-linear interactions in a synthetic network simulating intracellular calcium fluctuations of spiking neurons. We highlight the potential pitfalls of applying GC analysis on real in vivo calcium signals, and offer solutions regarding the choice of GC analysis parameters. We took advantage of calcium imaging datasets from motoneurons in embryonic zebrafish to show how the improved GC can retrieve true underlying information flow. Applied to the network of brainstem neurons of larval zebrafish, our pipeline reveals strong driver neurons in the locus of the mesencephalic locomotor region (MLR), driving target neurons matching expectations from anatomical and physiological studies. Altogether, this practical toolbox can be applied on in vivo population calcium signals to increase the selectivity of GC to infer flow of information across neurons.

RNA-binding proteins are central players in post-transcriptional regulation. Some of them, such as the well-studied bacterial RNA chaperones Hfq and ProQ or the eukaryotic RNAi factor Argonaute, interact with hundreds-to-thousands of different RNAs and thereby globally affect gene expression. As a shared yet limited resource, these and other RNA-binding hubs drive strong competition between their multiple ligands. This creates a ground for significant cross-communication between RNA targets, which enables them to share information, “synchronise” their behaviour, and produce interesting biochemical effects, sometimes propagating across the highly connected RNA-protein network. This property is likely universally present in hub-centred networks and plays a key role in global gene expression programmes. It is also an important factor in biotechnology and synthetic biology applications of RNA/protein-based circuits. However, few studies so-far focused on describing and explaining this phenomenon from first principles. Here we introduce an information theory-based framework to comprehensively and exactly describe information flow in hub-centred networks. We show that information sharing can achieve significant levels in relatively small networks, provided the hub is present in limiting concentrations. The transmitted information is sufficient to noticeably affect the binding probabilities of competing targets but drops exponentially along the network. Target overexpression can disrupt communication between other targets, while hub sequestration boosts the crosstalk. We also find that overlaps between the interactomes of two different hubs create both entropic challenges and new forms of long-range communication between RNAs and proteins.

Automatic machine translation plays an important role in reducing language barriers between people speaking different languages. Deep neural networks (DNN) have attained major success in diverse research fields such as computer vision, information retrieval, language modelling, and recently machine translation. Neural sequence-to-sequence networks have accomplished noteworthy progress for machine translation. Inspired by the success achieved by residual connections in different applications, in this work, we introduce a novel NMT model that adopts residual connections to achieve better performing automatic translation. Evaluation of the proposed model has shown an improvement in translation accuracy by 0.3 BLEU compared to the original model, using an ensemble of 5 LSTMs. Regarding training time complexity, the proposed model saves about 33% of the time needed by the original model to train datasets of short sentences. Deeper neural networks of the proposed model have shown a good performance in dealing with the vanishing/exploding problems. All experiments have been performed over NVIDIA Tesla V100 32G Passive GPU and using the WMT14 English-German translation task.

The article presents the results of a study on the application of a systematic approach to the formation and development of logistics supply chains in the construction complex of the Republic of Belarus in order to further assess its manageability, effectiveness, efficiency, investment attractiveness. This approach made it possible to structure the elements of the logistics system of the construction complex taking into account its properties: integrity and divisibility; connections; organization; integrative qualities. В статье приведены результаты исследования по применению системного подхода к формированию и развитию логистических цепей поставок в строительном комплексе Республики Беларусь с целью дальнейшей оценки его управляемости, результативности, эффективности, инвестиционной привлекательности. Этот под- ход позволил структурировать элементы логистической системы строительного комплекса с учетом ее свойств: целостности и делимости; связей; организации; интегративных качеств.

This study examines the potential of Shanghai crude oil (SCO) futures as a benchmark in the Asian market. We investigate the market efficiency and long-term equilibrium of SCO futures in comparison with global benchmarks, such as West Texas Intermediate, Brent, and Dubai crude oil futures. Despite the weak market integration between SCO futures and other international benchmarks, we find strong evidence that their market efficiency and long-term equilibrium do not significantly differ. We explain how current market properties are achieved using the information flow from international crude oil to the SCO futures market. Our findings present implications for investors and policymakers based on the unilateral information flow at the level and rise–fall pattern in the price series: (1) investors could exploit this pattern’s predictability in their investment strategy, and (2) regulators could implement open trading policies that would enable SCO futures to integrate with global benchmarks.

Accurate and complete diagnosis of nuclear accidents is the primary link to nuclear emergencies. The signed directed graph (SDG), as a common method of fault diagnosis, has good completeness. However, if there are feedforward and negative feedback control loops, compensation response and reverse response will be generated in the process of fault occurrence and propagation, which makes the state of the compensation variable and reverse variable uncertain, thus destroying the compatibility of the fault propagation path, leading to incomplete SDG reverse reasoning. In order to solve this problem, this paper proposes a SDG fault diagnosis method based on information flow. This method first finds out all the directed edges with the compensation variable or inverse variable node as the endpoint. By comparing the goodness of fit between the information flow on these directed edges and their total information flow curves, the path that contained the directed edge represented by sub-information flow with the highest R2 is obtained, and it is used as the fault propagation path with the maximum probability to determine the state of the compensation variable or inverse variable and fault location. At the same time, this paper extends the SDG model from the structure to build an FR-SDG model that can intuitively describe the node steady-state information under the final response and the fault propagation path with the maximum probability, which improves the diagnosis resolution. Finally, this paper carried out the relevant experiments in the subsystem of the nuclear reactor primary loop by using the simulation system PCTran AP1000, which verified the feasibility of this method.

This study analyzes the efficiency of the crude palm oil (CPO) futures market by conducting a variance ratio test and comparing it to the West Texas Intermediate (WTI) futures market. We discover that the weak-form efficient market hypothesis holds for both the CPO and WTI futures markets despite the significant difference in their liquidity. Using a scaling exponent, we investigate speculative trading activities and find that trading CPO futures in expectation of significant returns does not strongly involve a high level of risk unlike WTI futures. Our findings regarding market efficiency of the two futures markets are supported by the significant integration of the two with similar level of information flow from each market to the other. To explore the role of speculation in their market integration, we introduce a natural experimental setting using the coronavirus disease 2019 (COVID-19) pandemic, which caused a sudden decrease in the demand for fuel. The bidirectional information flow between the two markets is intensified after the COVID-19 pandemic due to lower level of speculation. The findings suggest that (i) stakeholders in the CPO market need to pay attention to the crude oil markets to anticipate its price changes, (ii) investors can use WTI futures as a hedging tool against CPO futures as long as there is mutual information flow, and (iii) regulators should carefully implement new CPO futures market policy, as either asymmetric changes in speculation or unbalanced regulation with the WTI futures market can create market distortion and regulatory arbitrage.

IntroductionIntracranial EEG (iEEG) data is a powerful way to map brain function, characterized by high temporal and spatial resolution, allowing the study of interactions among neuronal populations that orchestrate cognitive processing. However, the statistical inference and analysis of brain networks using iEEG data faces many challenges related to its sparse brain coverage, and its inhomogeneity across patients.MethodsWe review these challenges and develop a methodological pipeline for estimation of network structure not obtainable from any single patient, illustrated on the inference of the interaction among visual streams using a dataset of 27 human iEEG recordings from a visual experiment employing visual scene stimuli. 100 ms sliding window and multiple band-pass filtered signals are used to provide temporal and spectral resolution. For the connectivity analysis we showcase two connectivity measures reflecting different types of interaction between regions of interest (ROI): Phase Locking Value as a symmetric measure of synchrony, and Directed Transfer Function—asymmetric measure describing causal interaction. For each two channels, initial uncorrected significance testing at p < 0.05 for every time-frequency point is carried out by comparison of the data-derived connectivity to a baseline surrogate-based null distribution, providing a binary time-frequency connectivity map. For each ROI pair, a connectivity density map is obtained by averaging across all pairs of channels spanning them, effectively agglomerating data across relevant channels and subjects. Finally, the difference of the mean map value after and before the stimulation is compared to the same statistic in surrogate data to assess link significance.ResultsThe analysis confirmed the function of the parieto-medial temporal pathway, mediating visuospatial information between dorsal and ventral visual streams during visual scene analysis. Moreover, we observed the anterior hippocampal connectivity with more posterior areas in the medial temporal lobe, and found the reciprocal information flow between early processing areas and medial place area.DiscussionTo summarize, we developed an approach for estimating network connectivity, dealing with the challenge of sparse individual coverage of intracranial EEG electrodes. Its application provided new insights into the interaction between the dorsal and ventral visual streams, one of the iconic dualities in human cognition.

IntroductionSpatial working memory is a kind of short-term memory that allows temporarily storing and manipulating spatial information. Evidence suggests that spatial working memory is processed through three distinctive phases: Encoding, maintenance, and retrieval. Though the medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) are involved in memory retrieval, how the functional interactions and information transfer between mPFC and MD remains largely unclear.MethodsWe recorded local field potentials (LFPs) from mPFC and MD while mice performed a spatial working memory task in T-maze. The temporal dynamics of functional interactions and bidirectional information flow between mPFC and MD was quantitatively assessed by using directed transfer function.ResultsOur results showed a significantly elevated information flow from mPFC to MD, varied in time and frequency (theta in particular), accompanying successful memory retrieval.DiscussionElevated theta information flow, a feature that was absent on error trials, indicates an important role of the directional information transfer from mPFC to MD for memory retrieval.

Recent research into how musicians coordinate their expressive timing, phrasing, articulation, dynamics, and other stylistic characteristics during performances has highlighted the role of predictive processes, as musicians must anticipate how their partners will play in order to be together. Several studies have used information flow techniques such as Granger causality to show that upcoming movements of a musician can be predicted from immediate past movements of fellow musicians. Although musicians must move to play their instruments, a major goal of music making is to create a joint interpretation through the sounds they produce. Yet, information flow techniques have not been applied previously to examine the role that fellow musicians' sound output plays in these predictive processes and whether this changes as they learn to play together. In the present experiment, we asked professional violinists to play along with recordings of two folk pieces, each eight times in succession, and compared the amplitude envelopes of their performances with those of the recordings using Granger causality to measure information flow and cross-correlation to measure similarity and synchronization. In line with our hypotheses, our measure of information flow was higher from the recordings to the performances than vice versa, and decreased as the violinists became more familiar with the recordings over trials. This decline in information flow is consistent with a gradual shift from relying on auditory cues to predict the recording to relying on an internally-based (learned) model built through repetition. There was also evidence that violinists became more synchronized with the recordings over trials. These results shed light on the planning and learning processes involved in the aligning of expressive intentions in group music performance and lay the groundwork for the application of Granger causality to investigate information flow through sound in more complex musical interactions.

Information exchange between brain regions is key to understanding information processing for social decision-making, but most analyses ignore its dynamic nature. New insights on this dynamic might help us to uncover the neural correlates of social cognition in the healthy population and also to understand the malfunctioning neural computations underlying dysfunctional social behavior in patients with mental disorders. In this work, we used a multi-round bargaining game to detect switches between distinct bargaining strategies in a cohort of 76 healthy participants. These switches were uncovered by dynamic behavioral modeling using the hidden Markov model. Proposing a novel model of dynamic effective connectivity to estimate the information flow between key brain regions, we found a stronger interaction between the right temporoparietal junction (rTPJ) and the right dorsolateral prefrontal cortex (rDLPFC) for the strategic deception compared with the social heuristic strategies. The level of deception was associated with the information flow from the Brodmann area 10 to the rTPJ, and this association was modulated by the rTPJ-to-rDLPFC information flow. These findings suggest that dynamic bargaining strategy is supported by dynamic reconfiguration of the rDLPFC-and-rTPJ interaction during competitive social interactions.

This research describes a detailed analysis of the enactment of a lean Tool, the Last Planner System during pandemic period which helps in optimizing resources for better coordination among all stakeholders in a pandemic period. The LPS, as it is known, focusses on minimizing factors such as uncertainties, barriers, and variability to make construction projects more flexible for better project management. These include variations and deviations, supervision, delays in approvals, change resistance, and subcontractor dedication, which are all related to various stakeholders in the project. Following that, a Design Science Research technique is used to evaluate the effect of applying the LPS in buildings to address and achieve the objective of reducing the impact of setbacks created by the stakeholders such as architects, consultants, clients, project management team, etc., during the implementation of the LPS during any pandemic so as not to reduce the progress of execution. An action strategy is being used to attain this goal, and four case studies are documented which concern the implementation of the LPS in the building of the Boy’s Hostel, Research Scholar Quarters, Faculty Housing, and Girls’ Hostel at Chennai, Tamilnadu, India. Data was gathered by observation of site activities, interviews, documentation analysis, and a questionnaire survey and grouped into various factors. While adopting the LPS tool in research, the factors affecting the implementation in the COVID-19 pandemic period were found. Further, these factors were analysed, measured, ranked, and validated for adopting in projects.

The cooperative fusion with perception, communication and control is the inevitable trend of industrial Internet.Sorting out the research development and challenges of the cooperative fusion technologies with perception, communication and control in industrial Internet is of great significance to promote the development of industrial Internet.Firstly, the complicated coupling relationship among perception, communication and control in industrial Internet was introduced.Then, the related works and open problems of the cooperative fusion technologies with perception, communication and control were summarized.Finally, the future research directions were summarized and prospected for the problems of the cooperative fusion technologies with perception, communication and control in industrial Internet.

Adhering to a structured process of the flow of given to new topic information within academic written discourse is a significant challenge for most tertiary-level non-native EFL students. The progression of given to new topic information in written academic text is equally difficult for many non-native academic English writing teachers to distinguish, much less assess. This research seeks to determine if given to new information progression can be significantly identified, and explore the possibility of such topic progression being utilized as a practical form of academic writing assessment by experienced Thai tertiary teachers of academic English. Multiple Linear Regression was employed to determine the relationship between the Thai writing teachers' identification of the flow of given to new topic information and a preliminary analysis of topic information flow by a qualified native English writing teacher. The insights gained from this study show that the Thai academic English writing teachers could not significantly detect the presence of given to new topic information progression within the academic texts. The implication of this research suggests that at this time, the practical utilization of a given to new progression analysis may not be a feasible evaluative measure in written assessments for Thai academic English writing teachers.

Background: Information flow – information communication and transmission pathways and practices within healthcare systems – impacts patient journeys. Historically, regulating information flow was a key technology of reproductive governance in the Republic of Ireland. Pre-2018, law and the State sustained informational barriers to and through abortion care in Ireland. An expanded abortion service was implemented in January 2019. Method: Patient Journey Analysis (PJA) interrogates informational facilitators and barriers to/through post-2019 abortion care in Ireland. We focus on information flow at the interfaces between the ‘public’ sphere and ‘point of entry’, ‘point of entry’ and primary care, and primary and secondary care. Materials: The paper uses data from a mixed-method study. A tool for assessing online abortion service information (ASIAT), desktop research, and qualitative data from 108 in-depth interviews with providers, policy-makers, advocacy groups, and service users informed the analysis. Results: Abortion patient journeys vary. Information flow issues, e.g. communication of how to access services, referral systems, and information handover, act as barriers and facilitators. Barriers increase where movement from primary to secondary is needed. Applications: The article identifies good practice in information flow strategy, as well as areas for development. It illustrates the significance of information flow in accomplishing reproductive governance.