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The huge amount of data produced in the 6G networks not only brings new challenges to the reliability and efficiency of mobile devices but also drives rapid development of new storage techniques. With the benefits of fast access speed and high reliability, NAND flash memory has become a promising storage solution for the 6G networks. In this paper, we investigate a protograph-coded bit-interleaved coded modulation with iterative detection and decoding (BICM-ID) utilizing irregular mapping (IM) in the NAND flash-memory systems. First, we propose an enhanced protograph-based extrinsic information transfer (EPEXIT) algorithm to facilitate the analysis of protograph codes in the IM-BICM-ID systems. With the use of EPEXIT algorithm, a simple design method is conceived for the construction of a family of high-rate protograph codes, called irregular-mapped accumulate-repeat-accumulate (IMARA) codes, which possess excellent decoding thresholds and linear-minimum-distance-growth property. Furthermore, motivated by the voltage-region iterative gain characteristics of IM-BICM-ID systems, a novel read-voltage optimization scheme is developed to acquire accurate read-voltage levels, thus minimizing the decoding thresholds (in dB) of protograph codes. Analyses and simulations indicate that the proposed IMARA-aided IM-BICM-ID scheme and read-voltage optimization scheme remarkably improve the convergence and decoding performance of flash-memory systems. Thus, the proposed protograph-coded IM-BICM-ID can be viewed as a reliable and efficient storage solution for the new-generation mobile networks, such as Internet of Vehicles.

In a real-world setting, object instances from new classes can be continuously encountered by object detectors. When existing object detectors are applied to such scenarios, their performance on old classes deteriorates significantly. A few efforts have been reported to address this limitation, all of which apply variants of knowledge distillation to avoid catastrophic forgetting. We note that although distillation helps to retain previous learning, it obstructs fast adaptability to new tasks, which is a critical requirement for incremental learning. In this pursuit, we propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. This ensures a seamless information transfer via a meta-learned gradient preconditioning that minimizes forgetting and maximizes knowledge transfer. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection. We evaluate our approach on a variety of incremental learning settings defined on PASCAL-VOC and MS COCO datasets, where our approach performs favourably well against state-of-the-art methods., Comment: Published in IEEE Transactions on Pattern Analysis & Machine Intelligence, Nov 2021. Code is available in https://github.com/JosephKJ/iOD

Evolutionary multitasking (EMT) with the ability to tackle multiple different tasks has attracted more and more attention. The transferred knowledge among tasks can simultaneously improve the solving efficiency of all optimization problems in the evolutionary process. However, if the way of knowledge transfer is inappropriate, the negative knowledge transfer will make a bad effect on the performance of EMT algorithms. It is worth studying that how to promote the positive knowledge transfer across tasks. In this paper, a multiobjective multitasking algorithm named EMT-DAVT is introduced. The proposed algorithm contains two components, namely subspace distribution alignment (DA) strategy and decision variable transfer (VT) mechanism. In DA strategy, a learned mapping matrix is utilized to align the distributions in the subspaces and reduces the divergence between subpopulations belonging to different tasks. Then, VT mechanism is implemented which can further promote the positive information transfer. The two proposed strategies interact with each other to improve the knowledge transfer. Finally, the search engine is designed to balance exploration and exploitation. The experimental results on the multiobjective multitasking test suites demonstrate that EMT-DAVT outperforms other classical multiobjective multitasking evolutionary algorithms.

This article focuses on individualized degradation modeling and prognostics for a heterogeneous group, where each individual unit shows a distinct degradation process. Existing degradation models usually treat each unit separately and do not fully utilize the distinct characteristics of each individual. In this study, we propose a generic framework to handle the heterogeneity across units by effectively leveraging the intrinsic covariate information, which is closely related to the unit's degradation process. Specifically, we employ a multivariate Gaussian process (MGP) to nonparametrically establish the relation between the covariate information and degradation process. Through modeling the unit similarities based on the covariates, efficient information transfer among units is enabled for better degradation modeling and prognostics, as the collected degradation signals from one unit can be shared with the entire heterogeneous group. A theoretical justification for the proposed model is also investigated. Simulation studies are presented to evaluate the parameter estimation accuracy and the sensitivity of the proposed method. A case study on the Alzheimer's disease (AD) neuroimaging initiative data set is further conducted, which demonstrates the advantage of the proposed method over existing benchmark approaches.

The various information needs of users usually trigger a high curiosity about a particular topic. The existence of different information needs is what makes users transfer information to each other through social interaction. It is possible that this process occurs on a social media platform such as the rabbitholeid Instagram account. Information transfer and social interaction are related to each other because social interaction can be an important channel for the transfer of information between individuals or groups. The social interaction that causes the media transfer process is what occurs in the discussion in the comments column on the rabbitholeid account. The purpose of this research is to find out how the process of transferring information from the results of social interaction on the rabbitholeid Instagram account occurs. Through this study, it is hoped that later we will be able to find out how the process of transferring information that occurs on the account is made, especially with the social interactions that are carried out. In addition, this study also has problem limitations where the author will review in detail the cause and effect and the process of transferring information through social interaction on rabbitholeid accounts only so that it does not expand to a wider scope.

We explored how individual and age-related differences in working memory (WM) capacity affected subsequent long-term memory (LTM) retrieval. Unlike past studies, we tested WM and LTM not only for items, but also for item–color bindings. Our sample included 82 elementary school children and 42 young adults. The participants performed a WM task with images of unique everyday items presented sequentially at varying set sizes in different colors. Later, we tested LTM for items and item–color bindings from the WM task. The WM load during encoding constrained LTM, and participants with a higher WM capacity retrieved more items in the LTM test. Even when accounting for young children’s poor item memory by considering only the items that they did remember, they exhibited an exacerbated difficulty with remembering item–color bindings in WM. Their LTM binding performance, however, as a proportion of remembered objects, was comparable to that of older children and adults. The WM binding performance was better during sub-span encoding loads, but with no clear transfer of this benefit to LTM. Overall, LTM item memory performance was constrained by individual and age-related WM limitations, but with mixed consequences for binding. We discuss the theoretical, practical, and developmental implications of this WM-to-LTM bottleneck.

Multiobjective multifactorial optimization (MO-MFO), rooted in a multitasking environment, is an emerging paradigm wherein multiple distinct multiobjective optimization problems are solved together. This article proposes an evolutionary multitasking algorithm with learning task relationships (LTR) for MO-MFO. In the proposed algorithm, a procedure of LTR is well designed. The decision space of each task is treated as a manifold, and all decision spaces of different tasks are jointly modeled as a joint manifold. Then, through solving a generalized eigenvalue decomposition problem, the joint manifold is projected to a latent space while keeping the necessary features for all tasks and the topology of each manifold. Finally, the task relationships are represented as the joint mapping matrix, which is composed of multiple mapping functions, and they are utilized for information transfer across different decision spaces during the evolutionary process. In the empirical experiments, the performance of the proposed algorithm is verified and compared with several state-of-the-art solvers for MO-MFO on three suites of MO-MFO test problems. Empirical results demonstrate that the proposed algorithm surpasses other competitors on most test instances, and can well tackle complicated MO-MFO problems which involve distinct optimization tasks with heterogeneous decision spaces.

This paper investigates an intelligent reflecting surface (IRS) aided wireless powered communication network (WPCN) with a focus on multicast transmissions in a game-theoretic way, where a transmitter first harvests energy from a power station (PS) and then transmits the information to multiple Internet of Things (IoT) devices in a multicast form. An IRS is deployed to assist the wireless energy transfer (WET) and wireless information transfer (WIT) processes. Considering that the PS and the transmitter belong to different service providers, we propose two schemes based on Stackelberg game, namely IRS-aided transmitter dominant energy trading (IRS-TDET) with the transmitter as the leader and IRS-aided PS dominant energy trading (IRS-PDET) with the PS as the leader. To solve the non-convex optimization problem of the leader-level game in the IRS-TDET scheme, we first derive the closed-form optimal phase shifts in the WET stage, and then use the semidefinite relaxation approach to solve the max-min problem about the phase shifts of the WIT stage. Finally, a low-complexity alternating optimization algorithm is developed to solve the simplified non-convex optimization problem with regard to the energy price and WET time. Numerical results show that the performance of proposed IRS-TDET and IRS-PDET is superior to that of the corresponding non-IRS aided schemes, and the deployment of IRS can effectively improve the utilities of both the PS and the transmitter. In addition, it is preferred to deploy the IRS near the transmitter in WPCN, and the transmitter can be deployed near the PS or the IoT devices to obtain higher utility.

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

Brain computer interface (BCI) P300 speller can help severely disabled patients communicate and control with external machines or robots, so that the classification methods of P300 Electroencephalogram (EEG) signal play an important role in the development of BCI system and technology. In this paper, a novel support vector neural network (SVNN) is proposed and developed to obtain more accurate and effective EEG classification results. It is the first time to combine linear variational inequality (LVI) based primal-dual neural network with convex quadratic programming problem based on support vector machine to solve the classification problem. It has been proved that the support vector neural network globally converges to the optimal solution of convex optimization problem and iterates the parameters in the form of matrix, which means that the method has global convergence and parallelism. The proposed SVNN method is used to solve the classification problem of P300 EEG signals. Experimental results on data set IIb from BCI competition II and data set II from BCI competition III show that the accuracy of the proposed SVNN method is 100% and 98%, respectively. Compared with most of the state-of-theart algorithms, SVNN has the highest recognition accuracy and information transfer rate.

Existing multifactorial particle swarm optimization algorithms treat all particles equally with a consistent inter-task exemplar selection and generation strategy. This may lead to poor performance when the algorithm searches partial optimal areas belonging to different tasks at the later stage. In pedagogy, teachers teach students in different levels distinctively under their cognitive and learning abilities. Inspired by this idea, in this work, we devise a novel level-based inter-task learning strategy upon a dynamic local topology of inter-task particles. The proposed method separates particles into several levels and assigns particles to different levels with distinct inter-task learning methods. Specifically, we propose a level-based inter-task learning strategy to transfer sharing information among the cross-task neighborhood. By assigning the particles with diverse search preferences, the algorithm is able to explore the search space by using the cross-task knowledge, while reserving an ability to refine the search area. In addition, to address the issue of inter-task neighbor selection, we reform dynamically the local topology structure across the inter-task particles by methodical sampling, evaluating and selecting processes. Experimental results on the benchmark problems demonstrate that the proposed method enables the efficient cross-domain information transfer via the level-based inter-task learning.

“Event Projection Theory” (Iwamoto 2008) proposes a COMP (Composed of) function which delimits unbounded events and is contained within the conceptual structure of simple period modifying phrases (SP-modifying phrases) like “-kan”. However, the theory does not discuss the conceptual structure of operating period modifying phrases (OP-modifying phrases), such as “-kan kakete”, “-kan kakatte”, “-kan de”, modifying duration of bounded events. This study proposes that OP-modifying phrases also have COMP in their conceptual structure. However, this assumption leads to the following problem. Since OP-modifying phrases modify duration of bounded events and the COMP is applied to conceptual structures that already have the [+bounded] feature. However, [+bounded] feature cannot be delimited by COMP. Therefore, in order to apply the COMP to conceptual structure of bounded events, it is necessary to apply the un-delimiting function GR (Grinder) as a coercion. The process is outlined as follows. [(i)(1)Bounded event [+bounded] → (2)coecion (GR) [-bounded] → (3)OP-modifying phrase (COMP) [+bounded]] However, Iwamoto (2008) considers that the continuous application of inverse functions by coercion such as (i)(2) and (3) makes the information originally delimiting in (i)(1) meaningless and creates inappropriate conceptual structure. To address this issue, we propose that if the information originally delimiting in the conceptual structure is used for the calculation of the subsequently applied COMP, the information transfer will occur at the time of coercion, and the information will not become meaningless. This study proposes that the duration of operating period is calculated using the information of “limit point” where the event does not continue any longer. Therefore, we believe that when the COMP (OP-modifying phrase) in (i) is applied, the information of “limit point” delimiting in (i)(1) will be transferred for calculation of (i)(3), and the information does not become meaningless. As a result, the continuous application of inverse functions by coercion such as (i)(2) and (3) is acceptable.

Reflection modulation based on reconfigurable intelligent surface (RIS) is considered to be a promising information transfer mechanism without requiring any additional radio frequency chains. However, existing reflection modulation schemes consider manipulating the ON/OFF states of RIS elements, which suffers from power loss. In this paper, we propose a new scheme, called RIS-aided quadrature reflection modulation (RIS-QRM), for harvesting the reflection power based on an RIS-aided downlink multiple-input single-output (MISO) wireless system. To this end, RIS-QRM partitions RIS elements into two subsets for reflecting impinging signals into two orthogonal directions as well as passive beamforming and encodes its local data onto the element partition options. A closed-form expression for the unconditional pairwise error probability of RIS-QRM in Rician fading is derived assuming the maximum-likelihood detection. Moreover, we propose a low-complexity detection method with compromised performance for decoupling the joint search of the constellation symbol and the RIS element partition. Computer simulation results corroborate the effectiveness of RIS-QRM and validate the analytical results. It is shown that RIS-QRM is able to improve the error performance of the additional bits delivered by the RIS without deteriorating that of the bits carried on the constellation symbol, as compared to ON/OFF-based schemes.

Contains fulltext : 251158.pdf (Publisher’s version ) (Open Access) BACKGROUND: Insufficient information transfer is a major barrier in the transition from hospital to home. This study describes the systematic development and evaluation of an intervention to improve medication information transfer between hospital and community pharmacists. OBJECTIVE: To develop and evaluate an intervention to improve the medication information transfer between hospital and community pharmacists based on patients', community and hospital pharmacists' needs. METHODS: The intervention development and evaluation was guided by the six-step Intervention Mapping (IM) approach: (1) needs assessment to identify determinants of the problem, with a scoping review and focus groups with patients and healthcare providers, (2) formulation of intervention objectives with an expert group, (3) inventory of communication models to design the intervention, (4) using literature review and qualitative research with pharmacists and patients to develop the intervention (5) pilot-testing of the intervention in two hospitals, and (6) a qualitative evaluation of the intervention as part of a multicenter before-after study with hospital and community pharmacists. RESULTS: Barriers in the information transfer are mainly time and content related. The intervention was designed to target a complete, accurate and timely medication information transfer between hospital and community pharmacists. A pharmaceutical discharge letter was developed to improve medication information transfer. Hospital and community pharmacists were positive about the usability, content, and comprehensiveness of the pharmaceutical discharge letter, which gave community pharmacists sufficient knowledge about in-hospital medication changes. However, hospital pharmacists reported that it was time-consuming to draft the discharge letter and not always feasible to send it on time. The intervention showed that pharmacists are positive about the usability, content and comprehensiveness. CONCLUSION: This study developed an intervention systematically to improve medication information transfer, consisting of a discharge letter to be used by hospital and community pharmacists supporting continuity of care.

In recent years, many efficient evolutionary multitasking (EMT) algorithms have been proposed to solve multi-objective multi-task optimization problems. However, EMT algorithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission theory for solving multi-objective multitask optimization problems. First, two evolutionary operators inspired by cultural transmission theory are proposed. The elite-guided variation strategy can transfer the information from the current Pareto front to all individuals and guide the population to quickly converge. The horizontal cultural transmission strategy can efficiently transfer information from the source task to the target task. Second, to solve the negative transfer problem, an adaptive information transfer strategy is proposed to adaptively adjust the probability of an information transfer. Third, the proposed algorithm can gain a Pareto front with good convergence and diversity by utilizing a smaller population size and fewer computing resources. As a result, the proposed algorithm can effectively utilize the implicit similarity and complementarity between simultaneous optimized tasks to improve the overall convergence efficiency and reduce a negative transfer. Finally, comprehensive experimental results show that the proposed algorithm can achieve a better performance compared with previous state-of-the-art multi-objective EMT algorithms.

The article questions if human speech communication (SC) involves a transfer of information. The information functioning in speech communication is dwelled upon in the information and systemic activity approaches. The informational approach adequately explains only the direct method of information transfer, while the systemic activity approach is relevant for the sign-mediated speech communication typical for human interaction. The more heuristic thesis is that the perception of the chain of linguistic sign bodies produced in the intersubjective space only starts the construction of the perceived speech message content by the recipient. The completeness of the constructed speech message content depends entirely on the recipient, who has the optimal common consciousness with the speaker. The purpose of speech messages is not the actual construction of the content by the recipient, but the development of the message personal meaning. In human speech communication, the communicants do not transmit information, but use verbal signs bodies to actualize images of consciousness which are developed within a single ethnic culture and therefore are common for them. The incentive for the common consciousness development by the communicants is their participation in joint activities that ensure their earthly existence.

Molecular communication often involves coexisting links where certain links may have priority over others. In this work, we consider a system in three-dimensional (3-D) space with two coexisting communication links, each between a point transmitter and fully-absorbing spherical receiver (FAR), where the one link (termed primary) has priority over the second link (termed secondary). The system implements the underlay cognitive-communication strategy for the co-existence of both links, which use the same type of molecules for information transfer. Mutual influence of FARs existing in the same communication medium results in competition for capturing the information-carrying molecules. In this work, first, we derive an approximate hitting probability equation for a diffusion-limited molecular communication system with two spherical FARs of different sizes considering the effect of molecular degradation. The derived equation is then used for the performance analysis of primary and secondary links in a cognitive molecular communication scenario. We show that the simple transmit control strategy at the secondary transmitter can improve the performance of the overall system. We study the influence of molecular degradation and decision threshold on the system performance. We also show that the systems parameters need to be carefully set to improve the performance of the system.

A magnetic recording read channel has numerous parameters that must be carefully tuned for its best performance; these include not only the equalizer coefficients, but also any parameters inside the soft-output detector, some of which may be pattern-dependent, including signal levels, predictor coefficients, and residual noise variances. Conventional tuning strategies based on a minimum-mean-squared error criterion are not optimal in terms of frame-error rate and ultimately areal density. Here, we propose a strategy for optimizing the parameters with the aim of minimizing the frame-error rate after error-control decoding. The proposed strategy exploits the close connection between the frame-error rate and the gap between the two curves in an extrinsic information transfer (EXIT) chart. A stochastic gradient algorithm applied to a cost function that quantifies this gap leads to our proposed adaptive minimum-frame-error rate (AMFER) algorithm for adapting the equalizer and detector parameters. Numerical results based on a quasi-micromagnetic simulated channel show that the AMFER parameters can reduce the frame-error rate by more than two orders of magnitude, leading to a 7% gain in areal density over conventional minimum mean-square-error (MMSE) parameters.

Recent years have witnessed the successful development of health-related Internet-of-Things (IoT) devices, i.e., electroencephalographic (EEG), paving a ground-breaking way for better understanding the functionals in our brains. Despite massive research on EEG, there lacks an effective way to interpret complex EEG signals due to the shortage of informative EEG data, the challenge in capturing sophisticated connectivity patterns of EEG signals, and unfavorable results of the inherent noise associated with data collection. In this article, novel heterogeneous-domain deep learning is proposed to address these issues. Especially, we first propose a new scheme to extract the multilevel latent features using hybrid networks and provide two pathways for reconstructing the EEG signals. As a core part of the proposed method, the scheme considers the complex dependencies among adjacent EEG channels, spatiotemporal connectivity, and signal denoising. In addition, a novel consistency regularization method is proposed to enhance information sharing among the multilevel latent feature obtained from the labeled and unlabeled EEG samples, which is beneficial for both the information transfer and accelerating the training. Finally, we provide comprehensive case studies on the Lomonosov Moscow State University EEG data set, demonstrating that the proposed methods achieve superior performance than all the competing ones over a wide range of experimental settings.

Introduction: Rainfall data are required for planning, designing, developing and managing water resources projects as well as hydrological studies. Some previous studies have suggested increasing the density of the rain gauge network to reduce the estimation error. However, more operational stations require more installation costs and monitoring. Some common techniques including statistical methods, spatial interpolation, information-based theory and combination are used to evaluate and design the network. Chaharmahal va Bakhtiari province is a mountainous region; hence, a denser rainfall network is expected in this mountainous environment. The aim of this study was to evaluate the condition of rain gauge stations in Chaharmahal va Bakhtiari province using two approaches, i.e. geostatistical methods and entropy theory. Materials and Methods: The main required data set for this study is a time series of rainfall data. These data were collected on a daily scale from the Regional Water Company of Chaharmahal va Bakhtiari. After performing statistical tests, the annual data series was prepared for 46 rain gauge stations. A statistical period of 2000 to 2016 was used. The homogeneity of data was investigated by double mass test and histogram drawing methods using Excel and SPSS software, and the existence of trend in the time series of data was investigated by applying a Spearman test. Then, the adequacy of rain gauges in the gauging network was investigated. Annual rainfall interpolation maps and their standard error maps were prepared using the kriging method. Contribution of each station in reducing or increasing the error in the rain gauge network was investigated by removing each station in a cross validation procedure. The efficiency of the rain gauge network was evaluated using the concept of discrete entropy and the values of entropy indices. The value of keeping the rain gauge stations was determined using the net exchange information index. Results and Discussion: There was no homogeneity problem and significant trend in the data series. Considering the permissible error percentage of 5%, there is a need to add 15 new rain gauge stations to the network. To apply the geostatistical method, we applied it once without deleting any station; then, the kriging interpolation error was calculated for the precipitation data. Then, only one station was removed at each stage, and both the error and the contribution of each station in increasing or decreasing the error compared to the case without Station deletion were obtained. The results indicated that Ab-Turki, Shahrekord, Borujen and Barez stations were more important than other stations. Two stations namely Chaman-Goli and Ben stations can also be considered as the influential stations in error due to the density of stations in the region and error maps. Similarly, the results of the entropy theory method were found effective in evaluating the design of the rain gauge network. The highest value of H(x) was observed in the data of Armand station (3.26) and the lowest value was observed in Abbasabad station (2.28). Since H(x) shows the uncertainty of measuring data, the maximum and minimum uncertainty were found for Armand and Abbasabad sites, respectively. Based on the Net Exchange Information Index, Bardeh, Bareh Mardeh and Dezkabad stations were ranked 1 to 3, respectively, indicating that they transmit and receive more information than other stations. On the other hand, a number of stations including Dorak anari, Abtorki and Chelo stations had the lowest values. Conclusion: Due to the vast extent of the area and also considering the permissible error percentage of 5%, the number of the stations in this area was found to be insufficient. Thus, although calculating the kriging error maps showed that some stations do not have a significant share in increasing the error, removing the stations is not recommendable. Regarding the new stations, new 15 rain gauge stations are needed to check out the error maps. According to the field observations, the higher priority should be given to the northwestern area (which had the largest interpolation error) in the first place. For the regions with lower error, such as northeast, east, southeast, west and southwest that do not have rain gauge stations, additional rain gauge stations should be constructed.

The present study aims to highlight that there is a direct connection between the classroom management strategies involved in the computer-assisted lesson and students’ success in retaining information and building learning skills and competences. One of the roles that a teacher must assume in order to improve the instructional process is that of a classroom manager. As long as teaching is no longer understood as information transfer, teachers must learn efficient approaches of the contemporary multileveled instructional process. The aspects discussed in this paper regard the role of an active teacher, the student-centred instruction, building long term valid skills and competences, increasing students’ awareness and participation, managing discipline as a source of responsibilization, developing a positive teacher-student relationship, acquiring and using scientifically developed teaching strategies in the technologyaided classroom

In the context of global challenges, the issue of studying the interaction in innovation processes is especially relevant, because it is innovation that plays an important role in combating various types of challenges today. In turn, global challenges and the crisis are the impetus for innovative changes and shifts, innovation is the opposite of the crisis, because it is the innovative breakthrough that helps to overcome the crisis. The purpose of the article is analyzing the interaction of participants in the innovation process, determining their actions at different stages of the innovation process, barriers to the formation of stable socio-economic ties and indicators that determine the effectiveness of such interaction. For achievement this goal, the following methods were used: abstract and logical, system and synergetic, statistical, comparative, horizontal, vertical, tabular and graphical methods of information display. Results. The article identifies the types of interaction, analyzes the concept of innovation diffusion and actions of different actors for its implementation, analyzes the socio-economic interaction of participants in the innovation process, identifies their actions at different stages of the innovation process, barriers to stable socio-economic relations and indicators that determine the effectiveness of such interaction. Cybernetic, matrix and structural models of innovation process are analyzed. It is established that the innovation speed is greatly influenced by the state innovation policy, the existence of adequate information base, mechanisms of science and its interaction with production, forms of legislative decision-making on innovation, methods of information transfer, mechanisms. The practical significance of the research lies in the possibility of establishing effective interaction between participants in the innovation process by studying environments where the diffusion of innovations (relevant sites, social networks, research units and methodological departments of higher education institutions, research institutes, etc.), analysis of barriers to stable relationships between participants in innovation projects and existing models of the innovation process. Within the framework of further research it is planned to improve the system of indicators that determine the effectiveness of interaction during innovation processes, to determine the most key of them, to build a structural model of stable socio-economic interaction in the innovation diffuse network.

PurposeThis study aims to present a communication model for promoting value co-creation between citizens and policymakers and to draw out proposals on the rationale in implementing nudge effect as well as key policy implications. The paper also revisits the close relationship between information senders and receivers (citizens) from the perspective of “citizen behaviour change” and focuses on the nudge effect, which strengthens the communication skills of policymakers (information senders) and accelerates the behaviour change of citizens (i.e. the recipients of information). Based on the study, the authors propose a conceptual framework to explain the spontaneous incentive structure of citizens.Design/methodology/approachThis study uses a conceptual discussion based on a literature review. It examines the nudge effect on citizen behaviour based on information and communications models and theories. From critical discussions of literature, it proposes an analytical model that focuses on the nudge effect put forward in behavioural economics and takes into account the importance of measures to increase the sensitivity and empathy of the recipients of information.FindingsThe authors revisited the relationship between information senders and receivers from the perspective of citizen behavioural change and focus on the nudge effect, which strengthens the communication power of policymakers and accelerates the behavioural change of citizens (i.e. the recipients of information). According to the study, the authors propose a conceptual framework to explain citizens' spontaneous incentive structure. The dual perspective of policymakers and citizens should be central to the collaboration between citizens and policymakers to enhance the effectiveness of social policies, where the scope and type of value generated and the perspective of social value are essential.Research limitations/implicationsThis paper bridges the interdisciplinary research fields of behavioural economics and social policy. It is hoped that the model proposed in this paper will be an effective discussion framework for relevant researchers and practitioners when considering human resource training and system building related to information dissemination. In addition, it is hoped the model will be an effective tool for deepening discussions on topics that help to facilitate information transfer and communication within organisations.Practical implicationsThe realisation of policy intentions within the core elements of the social policy process, such as the definition of key objectives, policy mechanisms and legislation, are central elements of policy formation and are said to be highly similar across countries. The framework proposed in this study is a valid resource that can be applied in other countries and can be expected to act an effective guidepost for policy makers and other stakeholders engaged in social policy.Originality/valueUp to this point, in examinations of encouraging behavioural change in information recipients, attention has been focussed on the aspect of increasing the sender's ability to transmit information. Interventions, such as increasing the energy of the delivery, increasing the frequency and diversifying the media to make a strong impression on the receiver's consciousness, have been considered and implemented. However, this study suggests that, in addition to such efforts on the part of the sender, it is important to increase the sensitivity and affinity of the receiver to the message as preliminary preparation to receiving it.

Investor conference has been adapted from conference call but allows substantial managerial controllability over its timing and disclosures during the meeting. The announcement of holding such a me...

Visible thermal person reidentification (VT Re-ID) in the 6G-enabled Visual Internet of Things (VIoT) is essential for implementing 24-h surveillance in wide-area space. The existing methods mainly focus on reducing the visual difference between visible and thermal visual streams (V-streams). However, they cannot fully exploit the information of visible and thermal V-streams, which leads to suboptimal representations. In this article, we propose a novel deep network termed the mutual learning convolutional neural network (MLCNN) to transfer useful information between visible and thermal V-streams for VT Re-ID in 6G-enabled VIoT. The proposed MLCNN consists of the feature generation module and the mutual learning module. The feature generation module aims to map the input samples into a more discriminative feature space in order to decrease the intramodality difference. The mutual learning module employs the identification loss to supervise the predictive identity accuracy and utilizes the Kullback–Leibler divergence to constitute the mutual learning loss for useful information transfer. Extensive experiments on two popular visible thermal data sets (SYSU-MM01 and RegDB) prove the effectiveness of the proposed MLCNN.

This paper designs and analyzes the bit-interleaved coded modulation with iterative demapping and decoding (BICM-ID) framework under color-shift-keying (CSK) modulation to achieve spectrally efficient transmissions in visible light communication (VLC) systems. Specifically, we investigate the protograph low-density parity-check (LDPC) codes, bit-to-symbol mapping schemes, and constellation labeling rules in higher-order CSK-aided VLC systems. To fully explore the BICM-ID benefit, we propose an irregular CSK mapping, in which an $M$ -ary standardized constellation (called “Standard constellation” ) and a new $M$ -ary constellation (called “Target constellation” ) are separately applied to modulate two different coded sub-blocks in a codeword to optimize the error performance of the CSK-aided VLC systems. Furthermore, using a modified protograph extrinsic information transfer (PEXIT) algorithm, we construct two types of protograph LDPC codes (i.e., unpunctured and punctured protograph LDPC codes) that outperform the conventional protograph LDPC codes. Both analytical and simulation results show that the proposed irregular-CSK-mapped BICM-ID employing the new protograph LDPC codes and Target constellation is capable of achieving outstanding performance, and is a promising transmission solution for high-speed VLC applications.

Biology and electronics are both expert at receiving, analyzing, and responding to information, yet they use entirely different information processing paradigms. Biology processes information using networks that are intrinsically molecular while electronics process information through circuits that control the flow of electrons. There is great interest in coupling the molecular logic of biology with the electronic logic of technology, and we suggest that redox (reduction-oxidation) is a uniquely suited modality for interfacing biology with electronics. Specifically, redox is a native biological modality and is accessible to electronics through electrodes. We summarize recent advances in mediated electrochemistry to direct information transfer into biological systems intentionally altering function, exposing it for more advanced interpretation, which can dramatically expand the biotechnological toolbox.

Data communication and computer networks have enormously grown in every aspect of businesses. Computer networks are being used to offer instantaneous access to information in online libraries around the world. The popularity and importance of data communication has produced a strong demand in all sectors job for people with more computer networking expertise. Companies need workers to plan, use and manage the database system aspects of security. The security policy must apply data stored in a computer system as well as information transfer a network. This paper aimed to define computer data backup policies of the Incremental backup by using Unison synchronization as a file-synchronization tool and load balancing file synchronization management (LFSM) for traffic management. The policy is to be able to perform a full backup only at first as a one time from obtaining a copy of the data. The easiest aspect of value to assess is replacement for restoring the data from changes only and processing the correct information. As a result, the new synchronization technique was able to improve the performance of data backup and computer security system.

Information security, reliability of data transfer are today an important component of the globalization of information technology. Therefore, the proposed work is devoted to highlighting the results of the design and development of a hacking-resistant algorithm to ensure the integrity of information transfer via digital technology and computer engineering. To solve such problems, cryptographic hashing functions are used. In particular, elements of deterministic Chaos were introduced into the developed cyclic hashing algorithm. The investigation analyzes in detail the strengths and weaknesses of known hashing algorithms. They are shown to have disadvantages. The main ones are a large number of matches (Hamming (x, y) and the presence of a weak avalanche effect, which lead to a significant decrease in the reliability of the algorithm for hacking. The designed hashing algorithm uses an iterative Merkley-Damgard structure, augmented by the input message to a length multiple of 512bits. Processing in blocks of 128-bit uses cellular automata with mixed rules of 30, 105 and 90, 150 and takes into account the dependence of the generation of the initial vector on the incoming message. This allows half of the 10,000 pairs of arbitrary messages to have an inverse Hamming distance of 0 to 2. The proposed algorithm is four times slower than the well-known family of "secure hash algorithms." However, computation speed is not a critical requirement for a hash function. Decreasing the sensitivity to the avalanche effect allows the generation time to be approximately halved. Optimization of the algorithm, as well as its testing was carried out using new technologies of the Java programming language (version 15). Suggestions and recommendations for improving this approach to data hashing are given also

Developments in information and related technologies have led to a wider use of the Internet of things (IoT). By integrating both virtual and physical worlds, IoT creates an integrated communication framework of interrelated things and operating systems. With the advent of IoT systems based on digital remote care, transferring medical data is becoming a daily routine. Healthcare is one of the most popular IoT applications and tries to monitor patients’ vital signs during the day for weeks and to eliminate the need for hospitalization. In a healthcare system, many sensors are installed to collect the patient’s information, including environmental monitoring sensors and vital and unstructured message sensors in order to reduce the patients’ expenses. The IoT network contains flexible sensors in dynamically changing environments where sensors collect environmental information and send it to nursing stations for healthcare applications. Due to the wireless nature of IoT networks, secure data transmission in the healthcare context is very important. Data collected from sensors embedded in healthcare devices may be lost for various reasons along the transmission path. Therefore, establishing a secure communication path in IoT networks in the context of healthcare is of great importance. In this paper, in order to provide a reliable data transfer protocol in the context of healthcare, a reliable routing using multiobjective genetic algorithm (RRMOGA) method is presented. The contribution of this paper can be summarized in two steps: (i) using a multiobjective optimization approach to find near-optimal paths and (ii) using reliable agents in the network to find backup paths. The simulation outcomes reveal that the proposed approach, based on the use of the multiobjective optimization approach, tries to find optimal paths for information transfer that improve the main parameters of the network. Also, the use of secure agents leads to a secure information transfer in the network in the context of healthcare. Experimental results show that the proposed method has achieved reliability and data delivery rates, 99% and 99.9%, respectively. The proposed method has improved network lifetime, delivery rate, and delay by 14%, 2%, and 5.6%, respectively.

Bit-interleaved coded modulation with iterative decoding (BICM-ID) is a key technology enabling the communication systems to realize reliable and efficient data transmission. In this work, we present a study on the adaptive irregular-mapping (AIM)-aided BICM-ID systems with the use of protograph low-density parity-check (PLDPC) codes. To be specific, we first propose a three-step design strategy to generate a type of adaptive constellations, which can be combined with their corresponding initial constellations to formulate an AIM scheme for improving the BICM-ID performance. Then, we put forward a theoretical-analysis tool, called AIM-based protograph extrinsic information transfer (PEXIT) algorithm , to estimate the convergence performance of PLDPC codes in the AIM-aided BICM-ID systems. By making use of such an algorithm, we further develop a code-design method to construct a new type of PLDPC codes, called enhanced PLDPC codes , tailored for the AIM-aided BICM-ID systems. Both convergence analysis and simulations indicate that the proposed AIM-aided BICM-ID systems perform better than the existing counterparts, and their performance can be significantly improved by using the enhanced PLDPC codes. Consequently, the proposed designs have great potential to meet the high-reliability, high-throughput, and strong link-adaptation requirements of future wireless-communication scenarios, such as green Internet of Things (IoT).

In this mymargin letter, we investigate a backbone-assisted wireless powered communication network (WPCN) under the protocol of “harvest-then-transmit” protocol, where user nodes (UNs) harvest energy from the sinks in the downlink (DL), then they use the energy to transmit signals to the sinks by a non-orthogonal multiple access in the uplink (UL). We consider joint successive interference cancellation and backbone-assisted sink cooperation to reduce the impact of co-channel interference on the throughput performance. We maximize the system throughput by jointly optimizing user scheduling and the time allocation for DL energy harvesting and UL transmission. For solving the maximum throughput problem efficiently, we propose a column generation method based on a greedy algorithm. Numerical results show that our proposed energy and information transfer design is superior to other reference methods in WPCNs.

The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.

In this paper, the backward-compatible coding schemes are explored to achieve the near-capacity performances in dynamic LDPC-coded Gaussian multiple access channels (GMAC). Specifically, some user-load-compatible (ULC) masked edges in a raptor-like protograph are masked with the variation of user load $u$ . To distinguish the edges in a protograph, a multi-user protograph-based extrinsic information transfer (MU-PEXIT) chart is developed. Based on this chart, an iterative edge selection algorithm combined with a theoretical simplification is proposed to facilitate the selection of independent masked edge sets (MESs) for varying $u$ . To further decrease the complexity of the independent masking scheme, a nested version is presented, where the MESs for varying $u$ are nested and derived from the same mother MES. Meanwhile, nested selection algorithms are provided to select the nested MESs. Extensive simulation results demonstrate that the masked 5G-NR LDPC codes can maintain near-capacity performances regardless of the variation of $u$ . Besides, these ULC masked codes achieve comparable performances with the state-of-the-art LDPC codes which are specially re-designed in a static GMAC.

We investigate the effects of write-downs on market prices and volumes under fair value accounting. We also examine the prominent role that illiquidity plays in exacerbating the direct and spillover effects of exit valuation on equity and credit default swap (CDS) markets. Using hand-collected data on write-down announcements made during and after the 2007–2009 financial crisis, we find that firms that wrote down assets in accordance with fair value rules experience significant abnormal negative stock returns and spikes in the CDS premiums written on their obligations; similar firms without write-downs exhibit sympathetic and significant negative abnormal returns and positive premiums. We find that both the direct effect of the write-downs and the indirect spillover effects resulting from crisis-related illiquidity in the markets for financial assets (affecting the magnitude of write-downs) and in the securities markets (affecting the reaction to the write-downs) during the financial crisis go beyond normal direct and information transfer effects and may have contributed to the adverse consequences of the crisis. This paper was accepted by Shiva Rajgopal, accounting.

Wireless power transfer technologies such as simultaneous wireless information and power transfer (SWIPT) have shown significant potential to revolutionise the design of future wireless communication systems. When the only energy source is from the wireless signals that are mainly intended for information communications, the sustainability and outage performance of SWIPT systems become critical factors in theoretical evaluation and practical applications. This paper firstly models the energy harvesting and energy consumption of the power splitting protocol based SWIPT systems to investigate the general sustainability condition. We further model the power and information transfer outage probabilities using Markov Chains, which are unique for SWIPT systems since they both could cause communication outage. We further demonstrate how to apply the closed-form expression of the outage to optimise the key parameter of splitting ratio for SWIPT systems. Hardware and numerical experiments demonstrate the validity of the proposed model and outage analysis, and confirm the effectiveness of the solution to calculate the optimal splitting ratios under different signal and channel conditions.

Wireless communication technology is rapidly progressing due to its high quality and high speed of information transfer from one location to another. It is necessary to ensure the safety of wireless sensor networks with this progress. One of the main security concerns in WSNs is eavesdropping, an intrusion that collects information from other devices across the network. Eavesdropping attacks are insidious and it is hard to realise that they happen. When connected with a network, users can feed sensitive information inadvertently, such as passwords, account numbers, browsing, email content, etc. Security improvement in wireless network communication is therefore required.

Purpose The main concept analysis (MCA; Kong, 2009) quantifies the effectiveness and efficiency of information transfer during verbal discourse by means of four sets of sequential pictorial stimuli. This test was originally developed for a Cantonese-speaking population. The main goals of this study were (a) to translate and adapt the MCA to Dutch; (b) to establish normative data for healthy native Dutch-speaking adults; (c) to assess the effect of age, education level, and gender on MCA outcome; and (d) to establish inter- and intrarater reliability of the Dutch MCA. Method Language samples were collected from 60 healthy native Dutch speakers, equally recruited in different age (20–39 years, 40–59 years, 60–79 years) and education (middle and highly educated) categories through administration of the MCA. Utterances produced by at least 75% of the participants were included as a main concept in the Dutch MCA. Subsequently, age-specific normative data were established for each of the MCA parameters. Finally, an ICC was calculated in order to verify inter- and intrarater reliability of the Dutch MCA. Results The translated MCA consisted of 19 main concepts. Age-specific normative data were obtained. Both age and education level had a significant effect on MCA outcome. Information transfer in elderly was both less effectively and efficiently compared to young- and middle-aged adults. In addition, highly educated participants transferred information less efficiently compared to middle educated participants. Based on inter- and intrarater reliability measures, the Dutch MCA proved to be a reliable measuring instrument. Conclusions The MCA was translated to Dutch, and age-specific normative data were established for a healthy, Dutch-speaking population. The Dutch MCA is a reliable tool for eliciting and quantifying discourse production. Validation of the test for people with aphasia is necessary in order for the test to be useful in a clinical practice.

Motivation The emergence of single-cell RNA sequencing (scRNA-seq) has led to an explosion in novel methods to study biological variation among individual cells, and to classify cells into functional and biologically meaningful categories. Results Here, we present a new cell type projection tool, Hierarchical Random Forest for Information Transfer (HieRFIT), based on hierarchical random forests. HieRFIT uses a priori information about cell type relationships to improve classification accuracy, taking as input a hierarchical tree structure representing the class relationships, along with the reference data. We use an ensemble approach combining multiple random forest models, organized in a hierarchical decision tree structure. We show that our hierarchical classification approach improves accuracy and reduces incorrect predictions especially for inter-dataset tasks which reflect real-life applications. We use a scoring scheme that adjusts probability distributions for candidate class labels and resolves uncertainties while avoiding the assignment of cells to incorrect types by labeling cells at internal nodes of the hierarchy when necessary. Availability and implementation HieRFIT is implemented as an R package, and it is available at (https://github.com/yasinkaymaz/HieRFIT/releases/tag/v1.0.0). Supplementary information Supplementary data are available at Bioinformatics online.

Background Medication errors can occur because of incomplete or poorly communicated information at the transition from hospital to community. Following an audit in 2016, a project was undertaken to determine if pharmacists could improve the quality of medication information in discharge summaries by introducing a discharge medication reconciliation process. Pharmacists recorded any changes to the patient's medication in the electronic prescribing system during their inpatient stay and summarised these changes on discharge. Objective To compare medication information in discharge summaries with recognised standards for the clinical structure and content of patient records, and to assess the impact of the pharmacist process on compliance with certain elements of these standards. Setting A 750 bed teaching district general hospital in England. Method A retrospective observational study examining all patient discharge summaries over a 1 week period for compliance to national standards. Main outcome measure The main outcome measures were compliance with standards for medication started, stopped or changed in hospital and any differences between extent of recording this information by doctors and pharmacists. Results Data were collected and analysed for 243 patients, of whom 94 (38.7%) attracted a discharge medicines reconciliation process by a pharmacist. Discharge summaries were compliant with basic standards for changed medication in 42% of patients or 51.4% with the input of a pharmacist. This increase of 9.4% was statistically significant (p = 0.0365). At an enhanced level, pharmacists increased compliance from 39.1 to 46.5%, this did not represent a significant increase (p = 0.0989). Conclusion Pharmacists undertaking a discharge medication reconciliation process significantly improves the quality of discharge summaries.

This letter explores shortening technology for achieving near Gaussian multiple access channel (GMAC) capacity in low-density parity-check coded multi-user systems. To optimize the shortening patterns, a hybrid extrinsic information transfer (H-EXIT) tool, which integrates EXIT and protograph-based EXIT, is firstly developed. Based on this analysis, H-EXIT priority (HEP) algorithm is proposed to facilitate the optimization of shortening patterns. It can be observed that columns with larger degrees are prior to be selected, which differs from those for point-to-point scenarios. Inspired by this finding, we further propose largest-column-degree priority (LCDP) algorithm, which narrows the selection space to lower the complexity while maintains a comparable performance. Extensive simulation results demonstrate the superiority of proposed shortening schemes from two aspects: 1) Proposed shortening can bring nonnegligible gain over unshortening with consistent sum rate; 2) HEP and LCDP algorithms outperform benchmark algorithms.

The relationship between the feature-tuning curve and information transfer profile of individual neurons provides vital insights about neural encoding. However, the relationship between the spatial tuning curve and spatial information transfer of hippocampal place cells remains unexplored. Here, employing a stochastic search procedure spanning thousands of models, we arrived at 127 conductance-based place-cell models that exhibited signature electrophysiological characteristics and sharp spatial tuning, with parametric values that exhibited neither clustering nor strong pairwise correlations. We introduced trial-to-trial variability in responses and computed model tuning curves and information transfer profiles, using stimulus-specific (SSI) and mutual (MI) information metrics, across locations within the place field. We found spatial information transfer to be heterogeneous across models, but to reduce consistently with increasing degrees of variability. Importantly, whereas reliable low-variability responses implied that maximal information transfer occurred at high-slope regions of the tuning curve, increase in variability resulted in maximal transfer occurring at the peak-firing location in a subset of models. Moreover, experience-dependent asymmetry in place-field firing introduced asymmetries in the information transfer computed through MI, but not SSI, and the impact of activity-dependent variability on information transfer was minimal compared to activity-independent variability. Biophysically, we unveiled a many-to-one relationship between different ion channels and information transfer, and demonstrated critical roles for N-methyl-D-aspartate receptors, transient potassium and dendritic sodium channels in regulating information transfer. Our results emphasize the need to account for trial-to-trial variability, tuning-curve shape and biological heterogeneities while assessing information transfer, and demonstrate ion-channel degeneracy in the regulation of spatial information transfer.

This paper studies an intelligent reflecting surface (IRS) aided wireless powered sensor network (WPSN). Specifically, a power station (PS) provides wireless energy to multiple internet of thing (IoT) devices which supports them to deliver their own messages to an access point (AP). Moreover, we deploy an IRS to enhance the performance of the WPSN by intelligently adjusting the phase shift of each reflecting element. To evaluate the performance of the IRS assisted WPSN, we maximize its sum throughput to jointly optimize the phase shift matrices and the transmission time allocations. Due to the non-convexity of the formulated optimization problem, we first derive the optimal phase shifts of the wireless information transfer (WIT) in closed-form. Consequently, a semi-definite programming (SDP) relaxed approach is considered to jointly design the phase shift matrix of the wireless energy transfer (WET) and the transmission time allocations. In addition, we propose a low complexity scheme to gain insights and reduce the computational complexity incurred by the SDP relaxed scheme. Specifically, the optimal solutions of the phase shifts and the transmission time allocation are derived in closed-form by the Majorization-Minimization (MM) algorithm, the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. Finally, numerical results are presented to validate the proposed schemes and confirm the beneficial role of the IRS in comparison to the benchmark schemes, where the proposed IRS assisted scheme achieves almost 100% higher sum throughput, in comparison to the counterpart without IRS.

Content-sharing is one of the major applications of vehicular networks. To fully utilize the spectrum and the connection time, rate-compatible codes are required when sharing content. In this paper, we present a simple and flexible method to construct low-complexity rate-compatible codes for content sharing. We first present a novel construction framework for rate-compatible codes via recursive block Markov superposition transmission (rBMST). In the proposed construction, the shared content is partitioned into equal-length data chunks and transmitted directly, while their replicas are taken as the inputs of a given number of parallel systematic encoders to generate parity-check chunks. These parity-check chunks are then transmitted in parallel in a recursive block Markov superposition transmission manner. The proposed construction is flexible in the sense that codes with arbitrary rates can be obtained by adjusting the number of parallel rBMST encoders and the number of randomly punctured bits. We show that the simplest construction, using repetition to generate the parity-check chunks, leads to high-performance and low-complexity rate-compatible rBMST (RC-rBMST) codes. Specifically, the extrinsic information transfer (EXIT) chart analysis shows that asymptotic thresholds of the repetition-based RC-rBMST (RB-RC-rBMST) codes are within 0.25 dB of the channel capacities for a wide range of coding rates. Numerical results are presented to confirm the advantages of the RB-RC-rBMST codes in performance and complexity. Particularly, the RB-RC-rBMST codes perform as well as BMST-R codes but with much lower computational complexities.

When selecting feeding, hiding, or resting areas, animals face multiple decisions with different fitness consequences. To maximize efficiency, individuals can either collect personal information, or use information gathered and transmitted by other individuals (social information). Within group living species, organisms often specialize in either generating social information or using information gathered by other groups members. That is the case of the Spix’s disk-winged bat, Thyroptera tricolor. This species uses contact calls during roost finding. Social groups are composed by a mix of vocal and non-vocal individuals and those vocal roles appear to be consistent over time. Moreover, their vocal behavior can predict roost finding in natural settings, suggesting that vocal individuals are capable of generating social information that can be used by other group members. To date, however, we do not know if when presented with social information (contact calls) during roost finding, vocal individuals will make more or less use of these cues, compared to non-vocal individuals. To answer this question, we broadcast contact calls from a roost inside a flight cage to test whether vocal individuals could find a potential roost faster than non-vocal individuals when they encounter sounds that signal the presence of a roost site. Our results suggest that non-vocal individuals select roost sites based primarily on social information, whereas vocal individuals do not rely heavily on social information when deciding where to roost. This study provides the first link between vocal behavior and the use of social information during the search for roosting resources in bats. Incorporating ideas of social roles, and how individuals decide when and where to move based on the use of social information, may shed some light on these and other outstanding questions about the social lives of bats. Universidad de Costa Rica/[560-B8-015]/UCR/Costa Rica UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Biología UCR::Sedes Regionales::Sede del Sur

In this study, causalities of COVID-19 across a group of seventy countries are analyzed with effective transfer entropy. To reveal the causalities, a weighted directed network is constructed. In this network, the weights of the links reveal the strength of the causality which is obtained by calculating effective transfer entropies. Transfer entropy has some advantages over other causality evaluation methods. Firstly, transfer entropy can quantify the strength of the causality and secondly it can detect nonlinear causal relationships. After the construction of the causality network, it is analyzed with well-known network analysis methods such as eigenvector centrality, PageRank, and community detection. Eigenvector centrality and PageRank metrics reveal the importance and the centrality of each node country in the network. In community detection, node countries in the network are divided into groups such that countries in each group are much more densely connected.

Early career researchers (ECRs) are important stakeholders leading efforts to catalyze systemic change in research culture and practice. Here, we summarize the outputs from a virtual unconventional conference (unconference), which brought together 54 invited experts from 20 countries with extensive experience in ECR initiatives designed to improve the culture and practice of science. Together, we drafted 2 sets of recommendations for (1) ECRs directly involved in initiatives or activities to change research culture and practice; and (2) stakeholders who wish to support ECRs in these efforts. Importantly, these points apply to ECRs working to promote change on a systemic level, not only those improving aspects of their own work. In both sets of recommendations, we underline the importance of incentivizing and providing time and resources for systems-level science improvement activities, including ECRs in organizational decision-making processes, and working to dismantle structural barriers to participation for marginalized groups. We further highlight obstacles that ECRs face when working to promote reform, as well as proposed solutions and examples of current best practices. The abstract and recommendations for stakeholders are available in Dutch, German, Greek (abstract only), Italian, Japanese, Polish, Portuguese, Spanish, and Serbian. Instituto de Patología Vegetal Fil: Kent, Brianne A. Simon Fraser University. Department of Psychology; Canadá Fil: Holman, Constance. Universitätsmedizin Berlin. BIH QUEST Center for Responsible Research. Berlin Institute of Health at Charité; Alemania Fil: Amoako, Emmanuella. Cape Coast Teaching Hospital. Department of Paediatrics and Child Health; Ghana Fil: Amoako, Emmanuella. University of Cape Coast. School of Medicine. Department of Paediatrics and Child Health; Ghana Fil: Antonietti, Alberto. Politecnico di Milano. Department of Electronics, Information and Bioengineering; Italia Fil: Azam, James M. Stellenbosch University. DSI-NRF Center of Excellence in Epidemiological Modelling and Analysis. Department of Mathematics; Sudáfrica Fil: Ballhausen, Hanne. Universitätsmedizin Berlin. BIH QUEST Center for Responsible Research. Berlin Institute of Health at Charité; Alemania Fil: Fil: Ballhausen, Hanne. Universitätsmedizin Berlin. Department of Paediatric Endocrinology and Diabetes, Charité; Alemania Fil: Bediako, Yaw . University of Ghana. West African Centre for Cell Biology of Infectious Pathogens; Ghana Fil: Belasen, Anat M. Cornell University. Society for Conservation Biology. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Carneiro, Clarissa F. D. Federal University of Rio de Janeiro. Institute of Medical Biochemistry Leopoldo de Meis; Brasil Fil: Chung Chen, Yen. New York University. Department of Biology; Estados Unidos Fil: Debat, Humberto Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina Fil: Debat, Humberto Julio. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina Fil: Weissgerber, Tracey L. Universitätsmedizin Berlin. BIH QUEST Center for Responsible Research. Berlin Institute of Health at Charité; Alemania

To investigate whether a humanoid robot in a clinical radiological setting is accepted as a source of information in conversations before MRI examinations of patients. In addition, the usability and the information transfer were compared with a tablet. Patients were randomly assigned to a robot or tablet group with their consent prior to MRI. The usability of both devices was compared with the extended System Usability Scale (SUS) and the information transfer with a knowledge query. Reasons for refusal were collected by a non-responder questionnaire. At the University Hospital Halle 117 patients were included for participation. There was no statistically significant difference in gender and age. Of 18 non-responders, 4 refused to participate partly because of the robot; for another 3 the reason could not be clarified. The usability according to SUS score was different with statistical significance between the groups in the mean comparison and was one step higher for the tablet on the adjective scale. There was no statistically significant difference in knowledge transfer. On average, 8.41 of 9 questions were answered correctly. This study is the first application, in a clinical radiological setting, of a humanoid robot interacting with patients. Tablet and robot are suitable for information transfer in the context of MRI. In comparison to studies in which the willingness to interact with a robot in the health care sector was investigated, the willingness is significantly higher in the present study. This could be explained by the fact that it was a concrete use case that was understandable to the participants and not a hypothetical scenario. Thus, potentially high acceptance for further specific areas of application of robots in radiology can be assumed. The higher level of usability perceived in the tablet group can be explained by the fact that here the interface represents a form of operation that has been established for years in all population groups. More frequent exposure to robots could also improve the response in the future.· patients accept humanoid robots in clinical radiologic situations. · at present they can only convey information as well as an inexpensive tablet. · future systems can relieve the burden on personnel..· Stoevesandt D, Jahn P, Watzke S et al. Comparison of Acceptance and Knowledge Transfer in Patient Information Before an MRI Exam Administered by Humanoid Robot Versus a Tablet Computer: A Randomized Controlled Study. Fortschr Röntgenstr 2021; 193: 947 - 954.ZIEL: Untersucht wurde, ob ein humanoider Roboter im klinisch-radiologischen Setting als Informationsgeber im Gespräch vor MRT-Untersuchungen von Patienten akzeptiert wird. Ergänzend wurden die Benutzerfreundlichkeit und die Informationsvermittlung mit einem Tablet verglichen. Patienten wurden bei Einwilligung vor einem MRT randomisiert einer Roboter- oder Tablet-Gruppe zugeteilt. Die Handhabbarkeit beider Geräte wurde mit der erweiterten System Usability Scale (SUS) und die Informationsvermittlung mit einer Wissensabfrage verglichen. Ablehnungsgründe wurden per Non-Responder-Fragebogen erhoben. Im Universitätsklinikum Halle wurden 117 Patienten zur Teilnahme eingeschlossen. Bei Geschlecht und Alter gab es keinen statistisch signifikanten Unterschied. Von 18 Non-Respondern lehnten 4 die Teilnahme teilweise wegen des Roboters ab; bei weiteren 3 konnte der Grund nicht geklärt werden. Die Handhabbarkeit nach SUS-Score war zwischen den Gruppen im Mittelwertvergleich statistisch signifikant unterschiedlich und war für das Tablet um eine Stufe auf der Adjektiv-Skala höher. Bei der Wissensvermittlung gab es keinen statistisch signifikanten Unterschied; im Mittel wurden 8,41 von 9 Fragen korrekt beantwortet. Diese Studie ist der erster Anwendungsfall im klinisch-radiologischen Setting eines humanoiden Roboters, der mit Patienten interagierte. Tablet und Roboter sind zur Informationsvermittlung im Kontext eines MRT geeignet. Im Vergleich zu Studien, in denen nach der Bereitschaft zur Interaktion mit einem Roboter im Gesundheitswesen gefragt wurde, ist diese Bereitschaft in der hier vorliegenden Studie deutlich höher. Dies könnte dadurch erklärt werden, dass es sich um einen konkreten, für die Teilnehmer begreiflichen Anwendungsfall handelte und nicht um ein hypothetisches Szenario. Damit ist von einer potenziell hohen Akzeptanz für weitere spezifische Einsatzgebiete von Robotern in der Radiologie auszugehen. Der in der Tablet-Gruppe wahrgenommene höhere Bedienkomfort lässt sich dadurch erklären, dass hier das Interface eine seit Jahren in allen Bevölkerungsgruppen etablierte Bedienform darstellt. Eine häufigere Exposition mit einem Roboter könnte auch hier die Response zukünftig verbessern.· humanoide Roboter werden von Patienten in klinisch radiologischen Situationen akzeptiert. · aktuell können sie Informationen dabei jedoch nur so gut wie ein preisgünstiges Tablet vermitteln. · zukünftige Systeme können das Personal entlasten..· Stoevesandt D, Jahn P, Watzke S et al. Comparison of Acceptance and Knowledge Transfer in Patient Information Before an MRI Exam Administered by Humanoid Robot Versus a Tablet Computer: A Randomized Controlled Study. Fortschr Röntgenstr 2021; 193: 947 – 954.

It is important to supply students with skills about text type and structure in educational environments. To have such skills gives the student many advantages in the process of comprehending, and evaluating any text. This study aims to bring out the opinions of Turkish language teachers and elementary school teachers about teaching text type and structure. In this study, a descriptive research design was employed and the participants consisted of 55 Turkish language teachers and elementary school teachers. Content analysis was utilized for data analysis. In this study, to check reliability and validity, the statements of participants were transfered directly, participant confirmation and the percent of agreement (approximately % 0, 81) between two researchers were used. It was found that Turkish language teachers and elementary school teachers generally adopted a different teaching approach, and used the text-based, production and conversion, information transfer, and audience-centred activities. The participants stated that there are different problems in Turkish textbooks concerning the teaching. It is suggested that studies should be carried out to solve the problems that teachers have stated in the teaching of text type and structure.