Information Exchange Research Articles

Research on intelligent classification of weld defects based on improved ResNeXt network

Welding technology is widely used in the connection of metal workpieces, and then due to improper operation and other reasons, small defects may occur in the welding, which will lead to welding failure and hazardous accidents. Therefore, it is very important to accurately identify welding defects. The automatic defect identification method based on deep learning learns features directly from the full-focus mapping of weld defects and automatically classifies them; the ResNeXt network is selected for improvement, and the SK convolution unit is introduced into the original ResNeXt-50 network model to enhance the adaptive adjustment of sensory field; in order to alleviate the problem of the point-by-point convolution in the ResNeXt-50 which consumes a large amount of In order to alleviate the problem that point-by-point convolution in ResNeXt-50 consumes a large amount of computation, 1×1 group convolution is used instead of the original point-by-point convolution, and channel rearrangement is introduced to alleviate the problem of weakened information exchange of groups caused by group convolution. After improvement, the ResNeXt-50 network model can achieve a classification accuracy of 98.2%, which is 5.5% more than that of the original ResNeXt-50 on this paper's dataset, and the classification accuracy after T-sne visualization also has obvious separation boundary and clustering effects.

Africa CDC spearheading the strengthening of health information exchange in Africa

Africa CDC spearheading the strengthening of health information exchange in Africa

Isolating Others ——Origins, Mechanisms and Evolution

The behavior of isolating others is the alienation of life self-organization of exclusion mechanism. Isolation in modern society has evolved into a daily political game pattern, and isolation, more generally, is a hidden psychological activity. This deep field of social philosophy has yet to be revealed and reflected so far. At the core of isolation is personality denigration, thus indicating the deep origin of the core concept of contemporary social science, “social exclusion”. The group custom of isolating dissidents suppresses the personality and innovation of free individuals and leads to gangs and closed rigidity of the community. Reflecting on and revealing the anti-modernization nature associated with isolation, while simultaneously indicating the institutional and mechanistic construction of China's modernized harmonious society goal. The division of labor and market exchange in modern society contains ethical implications neglected by popular understandings that emphasize individual competition: the differences formed based on functional differentiation and individual liberation in modern society not only stimulate competition, but also create an inherent need for solidarity among individuals in terms of complementary dependence and mutual attraction. The contemporary society centered on information exchange promotes this direction to a universal dependence mechanism. The modern concept of free personality is not individualism but the internal mechanism of collective social cohesion and innovative life. Exclusive isolation and its closed community no longer have the basis of a modern system.

Pharmaceutical Care in Primary Healthcare—A Study of Nurses’, Pharmacists’, and Physicians’ Experiences of Interprofessional Collaboration

Implementation of pharmaceutical care for the benefit of patients and health services has been highlighted worldwide. Interprofessional collaboration between nurses, pharmacists, and physicians may contribute to raising awareness of pharmacological challenges, increasing quality, and minimising errors in pharmaceutical care. This study aimed to investigate how nurses, pharmacists, and physicians experience interprofessional collaboration in pharmaceutical care within community healthcare in Norway. The study had an explorative and descriptive design with a qualitative approach. Individual interviews were conducted with 12 healthcare personnel with key roles in pharmaceutical care within community healthcare services. The data were analysed using systematic text condensation. The results revealed three categories and four subcategories: The category “Professional challenges” contained the subcategories “Blurred responsibilities” and “The importance of trust and continuity”. The category “Organisational barriers” contained the subcategories “Lack of information exchange and suitable communications channels” and “Lack of time and meeting places”. The third category was “Nurses—the important link”. This study reveals challenges to and factors of success in increasing high-quality and safe pharmaceutical care, knowledge that can be used in quality work in the community health services and as input in curriculum development for the three professions.

EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution

Human emotions are complex psychological and physiological responses to external stimuli. Correctly identifying and providing feedback on emotions is an important goal in human–computer interaction research. Compared to facial expressions, speech, or other physiological signals, using electroencephalogram (EEG) signals for the task of emotion recognition has advantages in terms of authenticity, objectivity, and high reliability; thus, it is attracting increasing attention from researchers. However, the current methods have significant room for improvement in terms of the combination of information exchange between different brain regions and time–frequency feature extraction. Therefore, this paper proposes an EEG emotion recognition network, namely, self-organized graph pesudo-3D convolution (SOGPCN), based on attention and spatiotemporal convolution. Unlike previous methods that directly construct graph structures for brain channels, the proposed SOGPCN method considers that the spatial relationships between electrodes in each frequency band differ. First, a self-organizing map is constructed for each channel in each frequency band to obtain the 10 most relevant channels to the current channel, and graph convolution is employed to capture the spatial relationships between all channels in the self-organizing map constructed for each channel in each frequency band. Then, pseudo-three-dimensional convolution combined with partial dot product attention is implemented to extract the temporal features of the EEG sequence. Finally, LSTM is employed to learn the contextual information between adjacent time-series data. Subject-dependent and subject-independent experiments are conducted on the SEED dataset to evaluate the performance of the proposed SOGPCN method, which achieves recognition accuracies of 95.26% and 94.22%, respectively, indicating that the proposed method outperforms several baseline methods.

TEXT CLASSIFICATION USING ADAPTIVE BOOSTING ALGORITHM WITH OPTIMIZATION OF PARAMETERS TUNING ON CABLE NEWS NETWORK (CNN) ARTICLES

The development of the era encourages advances in communication and information technology. This resulted in the exchange of information being faster because it is connected to the internet. One platform that provides online news articles is Cabel News Network (CNN), which has been broadcasting news on its website since 1995. The number of Cabel News Network news articles continues to increase, so news articles are categorized to make it easier for readers to find articles according to the category they want. Classification is a technique for determining the class of an object based on its characteristics, where the class label is known beforehand. One of the algorithms for classification is adaptive boosting (AdaBoost). The AdaBoost algorithm performs classification by building several weighted decision trees (stumps), then the class determination is based on the number of stumps with the highest weight. The AdaBoost algorithm can be combined with parameter tuning to avoid overfitting or underfitting resulting from a weak set of stumps. Therefore, this study implements the AdaBoost algorithm with parameter tuning on CNN news article classification. The data used in this study is CNN news article data from 2011 to 2022 sourced from the Kaggle page. The data is categorized into six classes, namely business, entertainment, health, news, politics, and sports. This study uses two evaluation metrics, namely the accuracy value and the confusion matrix to measure the performance of the AdaBoost algorithm. The accuracy value obtained is 0,78763, the precision value is 0.91, the recall value is 0.85, and the F1 score value is 0.88.

Hunger games search algorithm based on stochastic individual information for engineering design optimization problems

Abstract The hunger games search algorithm (HGS) is a newly proposed meta-heuristic algorithm that emulates hunger-driven foraging behaviors in a population. It combines fitness values to determine individual weights and updates them based on fitness value size, resulting in high adaptability and effective optimization. However, HGS faces issues like low convergence accuracy and susceptibility to local optima in complex optimization problems. To address these problems, an improved version called BDFXHGS is introduced. BDFXHGS incorporates a collaborative feeding strategy based on HGS's design advantages. Individuals approach others based on hunger degree, facilitating information exchange and resolving convergence and accuracy issues. BDFXHGS combines a disperse foraging strategy and a directional crossover strategy to enhance exploration and convergence speed. The paper conducts qualitative analysis and ablation experiments to examine the effectiveness of the strategies. Comparative experiments are performed using IEEE CEC 2017 benchmark functions to compare BDFXHGS with competitive algorithms, including previous champion algorithms in different dimensions. Additionally, BDFXHGS is evaluated on 25 constrained optimization problems from the IEEE CEC 2020 competition and 5 real engineering optimization problems. Experimental results show that BDFXHGS performs well on benchmarks and outperforms other algorithms in real-world applications.

UTILIZATION OF MUSLIM PUBLIC SPACES AS A MEDIUM FOR DISCUSSION OF HALAL FOOD PRODUCTS IN INDONESIA

Public space is formed from the development of communication and information ztechnology involving internet users in cyberspace. Public space is utilized as a forum for discussion related to halal food products in Indonesia by the Muslim community. This discussion related to Muslim public space uses a case study method to examine phenomena that exist on Instagram social media. The discussion results show that mass media can be used effectively to exchange information between individuals included in the cyber community to discuss various aspects of halal food products in Indonesia. The role of public space as a forum for discussion of halal food products can add insight and improve the quality of consumers in choosing halal food products.

Governing ‘ordinary’ uncertainty: Circulating information and everyday insecurity in Karachi

The governing of uncertainty has been studied extensively within the interdisciplinary field of security studies. However, existing scholarship on security-related uncertainty focuses on the problem of its governance from the standpoint of Western-political macro-governmental regulatory regimes. To both rescale as well as decolonize existing scholarship on governing security-related uncertainty, this article brings security studies scholarship in conversation with ethnographic accounts of everyday uncertainty in Global South contexts. As a concept tied to unpredictable security futures, it introduces ‘ordinary’ uncertainty as a routinized experiential terrain of insecurity. One that is anticipatorily navigated by social actors operating at the micro-social scale. Drawing on fieldwork on ordinary uncertainty in Karachi, this article calls attention to the incredible amount of time and energy spent by urban residents who try to ‘stay updated’ with the ever-shifting security situation in the Pakistani megacity. They do this by gathering, exchanging and making sense of information circulating in their social circles, on the street, news channels and/or on social media platforms. By critical practices of information production and exchange this article reveals the politics of governing ordinary uncertainty in an unequal sociopolitical context.

Decentralized Health Care System

Decentralized medical systems represent a shift in medical delivery that distributes rights and information across the network rather thanrelying on a central location. This model leverages blockchain technology to ensure medical records are secure, transparent, and tamper-proof. Patients can have more control overtheir information, allowing or revoking access as needed. Collaboration has improved, making the exchange of information between doctors easier. Smart contracts automate, secure, and streamline the billing and insurance process. This decentralized framework increases privacy by reducing the risk of serious data breaches

A Mechanical Antenna for Long-Wave Communication by Integrating Piezoelectric Material and Magnets

Abstract The Chu's limit imposes a significant challenge for traditional antennas operating at extremely low frequencies, as they require a large size due to the long operating wavelength, thus limiting their applicability. To reduce the antenna size, this paper proposes a vibrating beam system for long-wave communication that leverages the inverse piezoelectric effect, vibration theory, and Maxwell's equations. A prototype utilizing beam structures is developed and examined experimentally. Furthermore, the frequency modulation of the vibrating beam system and signal transfer protocol are investigated in detail. The experimental results demonstrate that exciting different vibration modes of the vibrating beam system leads to varied electromagnetic signals in specific rules, enabling long-wave communications and ensuring confidentiality. This work offers valuable insights into the potential of local information exchange among close-range platforms. It also highlights the promising approach of integrating piezoelectric material and magnets within the vibrating beam system, showcasing their potential towards practical applications in long-wave communication.

Silo mentality in teams: emergence, repercussions and recommended options for change

PurposeThis article summarizes practitioner observations on three research questions. First, the factors that lead to the emergence and persistence of such teams. Second, the repercussions of siloed teams. And third, practical suggestions and recommendations that practitioners can employ to prevent silo formation or address existing silos. This article thus complements recent academic work that has previously explored the formation of silos.Design/methodology/approachThe authors used the input of current team leads from a focus group along with their consulting experience to explore these three research questions. The team lead input and consulting expertise are integrated with academic research on silos.FindingsThe emergence and persistence of silos was mostly attributed to company characteristics (size, growth and stakeholder management) as well as communication inefficiencies (lack of role clarity and ownership within teams), which in turn were impacted by situational variables (pandemic and turnover). The authors noted the effect of team composition effects, team competition and organizational changes (rapid growth and restructuring) as potential contributors to the formation and persistence of silos. The team lead experts and our consulting experiences were congruent with the literature focused on repercussions of silos, from poor information exchanges to inefficiencies, divisions and perceived isolation of teams from the organization. Solutions focused on project organization and documentation as well as the adoption of new decision-making tools and practices, and the creation of more exchange and learning opportunities. The authors added additional options to promote more visibility, appreciation, proactive monitoring within teams and organizational identification initiatives.Originality/valueThe current article adds a pragmatic perspective to silos and how organizations can address these when they become problematic and hinder performance and collaboration.

POSSIBILITIES AND POTENTIAL OF PROMOTING A MICROENVIRONMENT BASED ON A CULTURE OF PEACE IN EDUCATIONAL INSTITUTIONS

Research reveals that the culture of peace is an actual phenomenon of people's social life, which is not only an approach to the prevention of violence and an alternative to the culture of war and violence but also creates a society that is probably only talked about in the context of utopia. In 2024, of course, it is not worth dreaming about utopia, however, educating the teaching staff about a micro-environment based on a culture of peace and its influence on the organization's operation contributes to the achievement of the goals of the learning process and a human-centered work environment. At the same time, an influential educational resource on cooperation and conflict resolution, decision-making, all human rights, communication, and information exchange is being developed. The publication highlights the possibilities of promoting a microenvironment based on a culture of peace, using the gamification process, and its pedagogical and psychological possibilities in improving the microenvironment of educational institutions in Liepāja, describing the empirical experience from case studies where was survey and game “Peace Please” sessions. The study aims to analyze the possibilities of using the gamification process based on the culture of peace in improving the microenvironment of educational institutions in Liepāja. The study results provide information about the game "Peace, please!" possibilities of use in educational institutions, indicating the essential thing common to the culture of peace and the organization's microenvironment. Problems specific to the micro-environment in educational institutions are identified and solutions are proposed for promoting a micro-environment based on a culture of peace.

The gut-airway microbiome axis in health and respiratory diseases.

Communication between the gut and remote organs, such as the brain or the cardiovascular system, has been well established and recent studies provide evidence for a potential bidirectional gut-airway axis. Observations from animal and human studies indicate that respiratory insults influence the activity of the gut microbiome and that microbial ligands and metabolic products generated by the gut microbiome shape respiratory immunity. Information exchange between these two large mucosal surface areas regulates microorganism-immune interactions, with significant implications for the clinical and treatment outcomes of a range of respiratory conditions, including asthma, chronic obstructive pulmonary disease and lung cancer. In this Review, we summarize the most recent data in this field, offering insights into mechanisms of gut-airway crosstalk across spatial and temporal gradients and their relevance for respiratory health.

Enhancing voxel-based dosimetry accuracy with an unsupervised deep learning approach for hybrid medical image registration.

Deformable registration is required to generate a time-integrated activity (TIA) map which is essential for voxel-based dosimetry. The conventional iterative registration algorithm using anatomical images (e.g., computed tomography (CT)) could result in registration errors in functional images (e.g., single photon emission computed tomography (SPECT) or positron emission tomography (PET)). Various deep learning-based registration tools have been proposed, but studies specifically focused on the registration of serial hybrid images were not found. In this study, we introduce CoRX-NET, a novel unsupervised deep learning network designed for deformable registration of hybrid medical images. The CoRX-NET structure is based on the Swin-transformer (ST), allowing for the representation of complex spatial connections in images. Its self-attention mechanism aids in the effective exchange and integration of information across diverse image regions. To augment the amalgamation of SPECT and CT features, cross-stitch layers have been integrated into the network. Two different 177 Lu DOTATATE SPECT/CT datasets were acquired at different medical centers. 22 sets from Seoul National University and 14 sets from Sunway Medical Centre are used for training/internal validation and external validation respectively. The CoRX-NET architecture builds upon the ST, enabling the modeling of intricate spatial relationships within images. To further enhance the fusion of SPECT and CT features, cross-stitch layers have been incorporated within the network. The network takes a pair of SPECT/CT images (e.g., fixed and moving images) and generates a deformed SPECT/CT image. The performance of the network was compared with Elastix and TransMorph using L1 loss and structural similarity index measure (SSIM) of CT, SSIM of normalized SPECT, and local normalized cross correlation (LNCC) of SPECT as metrics. The voxel-wise root mean square errors (RMSE) of TIA were compared among the different methods. The ablation study revealed that cross-stitch layers improved SPECT/CT registration performance. The cross-stitch layers notably enhance SSIM (internal validation: 0.9614vs. 0.9653, external validation: 0.9159vs. 0.9189) and LNCC of normalized SPECT images (internal validation: 0.7512vs. 0.7670, external validation: 0.8027vs. 0.8027). CoRX-NET with the cross-stitch layer achieved superior performance metrics compared to Elastix and TransMorph, except for CT SSIM in the external dataset. When qualitatively analyzed for both internal and external validation cases, CoRX-NET consistently demonstrated superior SPECT registration results. In addition, CoRX-NET accomplished SPECT/CT image registration in less than 6s, whereas Elastix required approximately 50s using the same PC's CPU. When employing CoRX-NET, it was observed that the voxel-wise RMSE values for TIA were approximately 27% lower for the kidney and 33% lower for the tumor, compared to when Elastix was used. This study represents a major advancement in achieving precise SPECT/CT registration using an unsupervised deep learning network. It outperforms conventional methods like Elastix and TransMorph, reducing uncertainties in TIA maps for more accurate dose assessments.

Setting Standards for Personal Health Data in the Age of 5G and 6G Networks

Electronic health records (EHRs) play a vital role in simplifying thorough and effective patient treatment, promoting smooth exchange of information between medical professionals, and enhancing the process of making clinical decisions. With the increasing adoption of sensor-embedded smart wearables and home automation devices, new opportunities arise for innovative solutions in various sectors, such as eHealth. In the age of 5G and 6G, the potential of utilizing user-collected health data becomes vast, promising significant improvements in people’s health and well-being. Realizing continuous healthcare access takes a step closer to reality by equipping EHRs to effectively store and interpret data collected by these sensors. This would result in personalized medical services that adhere to standardized practices. This paper presents a comprehensive review of contemporary advancements in the realm of standardization methods aimed at managing personal health data. The study delves into an extensive analysis of state-of-the-art solutions that have emerged to address the intricate challenges associated with the harmonization and uniformity of personal health information. By systematically examining these cutting-edge approaches, the review elucidates the diverse strategies employed to establish a cohesive framework for organizing, storing, and exchanging personal health data. Furthermore, the review critically evaluates the effectiveness and limitations of each solution in terms of promoting interoperability, safeguarding data privacy, and facilitating seamless data sharing among healthcare stakeholders. Furthermore, this paper then presents an approach to standardize the data by establishing semantic constraints for healthcare data types and proposing a validation procedure to ensure compliance with relevant standards and regulations.

Professional voice use in health and nursing care - Time for reconsideration? A scoping review.

Communication is a key tool in the nursing profession. It is known that listeners are sensitive to the speaker's voice and interpret the speaker's intentions primarily from the non-verbal signal conveyed. To map and discuss the current state of knowledge and research evidence on professional voice use in health and nursing care. A scoping review adherent to the EQUATOR guidelines PRISMA-ScR. Searches in the EBSCO, CINAHL, Medline, PubMed, PsychInfo and PsycArticles databases were conducted between 1 and 28 February 2023. According to the data (n = 23), health professionals use voice as a vital aspect of caring communication and caring relationships, and in leadership as well as inter-professional collaboration, to facilitate information exchange and understanding and promote shared decision-making. The data showed that there is also a rising tendency to explore vocal demands and problems among health professionals. No studies were found on the use of voice in digital health and nursing care services or by social robots. Most of the reviewed studies were cross-sectional, rather small, and focused primarily on the perspectives of healthcare professionals. The use of voice should be explored as a vital aspect of caring communication and caring relationships from the patients' perspective, and the effects of voice use on listeners' emotions and actions should be examined in different contexts with modern, voice-specific data collection methods. Studies focusing on voice use in digital health care and by social robots are also needed. By integrating training programmes, education and technological innovations, health care can leverage the full potential of voice-based communication to promote a more coordinated and patient-centred care environment, true inter-professional collaboration and effective leadership. Professional voice use is an essential part of all health and nursing care and an impressive method that should be used consciously. Therefore, the meaning and methods of voice use, including speech accommodation, should be systematically introduced into health and nursing care and included in nursing education.

Cryptography-based location privacy protection in the Internet of Vehicles

AbstractThe evolution of the Internet of Things paradigm in recent years demonstrate a significant impact on the transportation sector, leading to the emergence of a new research field, known as the Internet of Vehicles (IoV). In the IoV, vehicles can exchange information with each other and with the roadside units making use of Vehicular Ad Hoc Networks (VANETs). As this technology reaches near-to-market maturity levels, several issues arise related to the protection of users’ privacy, while the interest of adversaries for such private user data in IoV environments gets stronger. This paper aims to present a review of the state-of-the-art techniques tackling the protection of location privacy in IoV environments, as well as experimental evaluation findings regarding the usage of various cryptographic algorithms for the protection of information exchange in these networks. In the conducted evaluations, the AES algorithm has been used as the main standard, which has been coupled with several other encryption/decryption algorithms, such as RSA, ECC and NTRU. The metrics used for the evaluation include measurements over the key generation process, the certificate generation, the encryption/decryption times, the signature generation/verification times, etc. Moreover, the size of messages in the negotiation, the pseudonym exchange and the new pseudonym enabling phases has been recorded, while the energy consumption in the exchange pseudonyms phase has also been measured. All previous experiments have been carried out mainly on NS-3 and SUMO open-source software aiming to have an estimation of how the aforementioned algorithms behave under constrained resources such as CPU usage and power.

Complexity synchronization in living matter: a mini review

Fractal time series have been argued to be ubiquitous in human physiology and some of the implications of that ubiquity are quite remarkable. One consequence of the omnipresent fractality is complexity synchronization (CS) observed in the interactions among simultaneously recorded physiologic time series discussed herein. This new kind of synchronization has been revealed in the interaction triad of organ-networks (ONs) consisting of the mutually interacting time series generated by the brain (electroencephalograms, EEGs), heart (electrocardiograms, ECGs), and lungs (Respiration). The scaled time series from each member of the triad look nothing like one another and yet they bear a deeply recorded synchronization invisible to the naked eye. The theory of scaling statistics is used to explain the source of the CS observed in the information exchange among these multifractal time series. The multifractal dimension (MFD) of each time series is a measure of the time-dependent complexity of that time series, and it is the matching of the MFD time series that provides the synchronization referred to as CS. The CS is one manifestation of the hypothesis given by a “Law of Multifractal Dimension Synchronization” (LMFDS) which is supported by data. Therefore, the review aspects of this paper are chosen to make the extended range of the LMFDS hypothesis sufficiently reasonable to warrant further empirical testing.

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.