Information Exchange Research Articles

Demonstration of microwave single-shot quantum key distribution

Security of modern classical data encryption often relies on computationally hard problems, which can be trivialized with the advent of quantum computers. A potential remedy for this is quantum communication which takes advantage of the laws of quantum physics to provide secure exchange of information. Here, quantum key distribution (QKD) represents a powerful tool, allowing for unconditionally secure quantum communication between remote parties. At the same time, microwave quantum communication is set to play an important role in future quantum networks because of its natural frequency compatibility with superconducting quantum processors and modern near-distance communication standards. To this end, we present an experimental realization of a continuous-variable QKD protocol based on propagating displaced squeezed microwave states. We use superconducting parametric devices for generation and single-shot quadrature detection of these states. We demonstrate unconditional security in our experimental microwave QKD setting. The security performance is shown to be improved by adding finite trusted noise on the preparation side. Our results indicate feasibility of secure microwave quantum communication with the currently available technology in both open-air (up to ~ 80 m) and cryogenic (over 1000 m) conditions.

Institutional model of the financial services market and its development

In the paper, the institutional model of the financial services market in Ukraine is defined as a complex system that ensures the interaction of its various subjects, regulates their activities and contributes to the stable and effective functioning of the financial sector based on taking into account the role and relationships of public and private organizations, legal and regulatory frameworks, as well as socio-economic factors that affect the functioning of the financial services market. Three stages of the development of the financial services market in Ukraine are highlighted, their characteristics, achievements and challenges are given. The main components of the institutional model of the financial services market are defined. It is shown that high-quality infrastructure and effective debt settlement institutions are critical for maintaining the financial stability of credit institutions. The purpose of the reform of state regulation based on the model of a single regulator is determined, which consists in increasing the efficiency, transparency and stability of the financial market. The advantages of a single regulator based on the NBU are: consolidation of functions, improvement of efficiency, reduction of administrative costs, improvement of coordination, increase of transparency, reduction of duplication. The challenges are: the difficulties of the transition period, the need to maintain specialization and the implementation of management changes. It is shown that the financial ombudsman plays an important role in the system of institutions for professional regulation of financial services markets. At the initial stage of implementation of the said institute, its role as a mediator is critically important for creating an effective mechanism for resolving conflicts and ensuring trust in the financial market. It has been established that the financial ombudsman performs the functions of: an independent mediator based on the principles of neutrality and confidentiality; supports the resolution of conflicts based on the analysis of the situation and the proposal of solutions, improves communication by organizing meetings and ensuring a clear exchange of information; ensures transparency and fairness by clarifying rights and obligations, creating standards for mediation; increases confidence in the financial market on the basis of reducing conflicts and improving the reputation of the financial market.

Использование финансовой информации в сотрудничестве государств по выявлению и пресечению преступлений

The use of financial information in investigations plays the key role in crime combating. Efficient application of data on financial transactions facilitates identification and prevention of activities of transnational and organized criminal networks. However, limited opportunities of law enforcement authorities and the absence of access to centralized bank account registers can hinder such investigations. Enhancement of international cooperation and information exchange between law enforcement authorities and financial intelligence units (FIUs) can substantially increase the efficiency of investigations. Such cooperation includes training and professional development of employees (it is critically important to teach them contemporary financial data analysis methods and how to use cutting-edge technologies), development of new technologies. Innovative instruments of analysis of financial information are required for timely and accurate identification of criminal acts. Creation of flexible mechanisms of access to information requires development of international data exchange systems, which will enable law enforcement authorities to receive information on bank accounts without delay.

Tracking the burden, distribution, and impact of Post-COVID conditions in diverse populations for children, adolescents, and adults (Track PCC): passive and active surveillance protocols

BackgroundTrack PCC includes five geographic surveillance sites to conduct standardized population-based surveillance to estimate and track Post-COVID Conditions (PCC) by age, sex, race/ethnicity, geographic area, severity of initial infection, and risk factors among persons with evidence of SARS-CoV-2 infection (based on the Council of State and Territorial Epidemiologist [CSTE] case definitions for confirmed cases or laboratory-confirmed evidence of infection).MethodsThe study will estimate the incidence, prevalence, including temporal trends, and duration and severity of PCC symptoms, among children, adolescents, and adults. PCCs include a broad range of symptoms and conditions that continue or develop after acute SARS-CoV-2 infection or COVID-19 illness. Surveillance includes both passive and active components for diverse populations in Arizona, Indiana, and Utah as well as the Bronx Borough, NY, and part of Philadelphia County, PA. Passive surveillance will utilize electronic health records and health information exchanges within each site catchment area to longitudinally follow persons with COVID-19 to estimate PCC occurring at least 30 days after acute COVID-19 illness. Active surveillance will utilize self-report of PCCs from detailed surveys of persons ages 7 years and older with evidence of SARS-CoV-2 infection in the past 3 months. Respondents will complete follow-up surveys at 6-, 12- and 18-months post-infection.DiscussionThese data can help identify which groups are most affected by PCC, and what health differences among demographic groups exist, as well as indicate potential barriers to care. These additional levels of granularity can inform public health action and help direct needed clinical care for patients.

Empirical Study on the Relationship Between Quality of Nursing Services and Postoperative Satisfaction of Patients with Acute Appendicitis: Intermediation Based on Information Flows

This study investigated the relationship between nursing care and patient satisfaction, with a specific focus on postoperative satisfaction in patients with acute appendicitis. The shift in China's medical model from a biomedical approach to a biopsychosocial framework emphasizes patient-centered care, with patient satisfaction becoming a key indicator of service quality. The study used a survey-based approach to collect data from 384 patients in hospitals in Guangxi, China. Through statistical analysis, the study explores how the quality of care services, doctor-patient communication processes, and information exchange affect patient satisfaction. The research results show that there is a significant positive correlation between the quality of nursing services and patient satisfaction. In particular, the professional level and effective communication skills of nursing staff significantly improve patient satisfaction. This study provides practical recommendations for enhancing the training process in clinical settings.

Navigating inter-team competition: How information broker teams achieve team innovation.

Organizations are increasingly using teams to stimulate innovation. Often, these teams share knowledge and information with each other to help achieve their goals, while also competing for resources and striving to outperform each other. Importantly, based on their industry, the nature of work, or prior history, some teams may face more competition from peer teams than others. Our research examines how teams' competitive relations with other teams in the organization operate in tandem with their collaborative inter-team information exchange relations in impacting their innovation. Using two studies-a field study of 73 knowledge-intensive teams in high-tech engineering firms and a team-based network experimental study of 162 teams-we find that a high degree of overall competition with many peer teams reduces a focal team's ability to acquire and utilize diverse knowledge from these teams (i.e., inter-team knowledge integration), thereby hindering team innovation. However, applying insights from network structural hole theory, we find that when a focal team occupies a brokerage position in the inter-team information exchange network, this can help buffer the effects of competition in getting access to knowledge resources from other teams, thus enabling their innovation. Additionally, we find that focal broker teams' dealmaking and network obstruction behaviors explain these effects. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multi-population genetic algorithm with crowding-based local search for fuzzy multi-objective supply chain configuration

Supply chain configuration is often fuzzy and involves multiple objectives in real-world scenarios, but existing researches lack the exploration in the fuzzy aspect. Therefore, this paper establishes a fuzzy multi-objective supply chain configuration problem model to minimize the lead time and product cost oriented towards real supply chain environments. To solve the fuzzy problem, the theories of membership and closeness degree in fuzzy mathematics are adopted, and a multi-population genetic algorithm (MPGA) with crowding-based local search method is proposed. The MPGA algorithm uses two populations for optimizing the two objectives separately and effectively, and is characterized by three main innovative aspects. Firstly, a radical-and-radial selection operator is designed to balance the convergence speed and diversity of population. In the early stage of the algorithm, two populations are both optimized towards the ideal knee point, and then are separately optimized towards the two ends of the Pareto front (PF). Secondly, an elitist crossover operator is devised to promote information exchange within two populations. Thirdly, a crowding-based local search is proposed to speed up convergence by improving the crowded solutions, and to enhance diversity by obtaining new solutions around the uncrowded ones. Comprehensive experiments are tested on a fuzzy dataset with different sizes, and the integral of the hypervolume of PF is used for the evaluation of the fuzzy PF. The results show that MPGA achieves the best performance over other comparative algorithms, especially on maximum spread metric, outperforming all others by an average of 39 % across all test instances.

Parameters Identification Related to Design of Customer Relationship Management Structure for Health Tourism Facilitators in Iran: A Case Study of Iran

The Health Tourism Industry is burgeoning within Iran, presenting promising opportunities. Despite its relative infancy in the country, this sector predominantly comprises Small and Medium Enterprises (SMEs). These entities encounter a spectrum of challenges as they navigate the path to growth, with Customer Relationship Management (CRM) emerging as a prominent concern. Effective management of customer relations necessitates a comprehensive understanding of their diverse needs and challenges, spanning from initial engagement to the culmination of the relationship. Designing an effective CRM structure requires a meticulous exploration of these facets throughout the customer journey. In our study, we conducted a literature review to explore these challenges and employed a qualitative approach, conducting semi-structured interviews with seven Iranian health tourism facilitators. Through framework analysis, we identified key insights into CRM challenges and strategies. Our thematic analysis revealed seven pivotal themes germane to CRM within Iranian health tourism facilitation: Initial customer engagement; Transformation of information between customers and health tourism facilitator; Data assessment and Challenges in information exchange; Perception of service quality factors; Cultural and national considerations; Monitoring and oversight; and Stress mitigation strategies Keywords: customer relationship management, health tourism, health tourism facilitators, health care, customer journey

Enhancing Task-Oriented Dialogue Modeling through Coreference-Enhanced Contrastive Pre-Training

Pre-trained language models (PLMs) are proficient at understanding context in plain text but often struggle with the nuanced linguistics of task-oriented dialogues. The information exchanges in dialogues and the dynamic role-shifting of speakers contribute to complex coreference and interlinking phenomena across multi-turn interactions. To address these challenges, we propose Coreference-Enhanced Contrastive Pre-training (CECPT), an innovative pre-training framework specifically designed to enhance dialogue modeling. CECPT utilizes unsupervised dialogue datasets to capture both semantic richness and structural coherence. Our experimental results demonstrate that the CECPT model significantly outperforms established baselines in three critical applications: intent recognition, dialogue act prediction, and dialogue state tracking. These findings suggest that CECPT is more adept at following the information flow within dialogues and accurately linking statuses to their respective references.

Entropy exchange and coherent information based on local quantum Bernoulli noises

Entropy exchange and coherent information based on local quantum Bernoulli noises

FIX Protocol: The Backbone of Financial Trading

The Financial Information exchange, commonly known as the FIX protocol, is a key component in the trading market infrastructure. Hence, this paper delivers a clear background on the FIX protocol: its structure, role, and its importance in the financial markets space. In this paper, we look at the background and development of the FIX protocol as well as the functions and usefulness of this protocol in the conducting of trading electronically. Sect. 2 from the literature survey of various studies and application of the FIX protocol in conjunction with distinct financial markets. The 'Methodology' subchapter explains how this FIX protocol functions practically at the level of messages' formatting, sessions' employing, and an application of data protection measures. Last of all, the conclusion and recommendation, as well as findings on the effects of FIX protocol on trading efficiency, market transparency, and such costs, are also provided. The conclusion provides a brief resume of the papers and notes the outlook of the implication of the FIX protocol considering the ever-expanding digital frontiers in the financial sector.

Adaptive Transmission Interval-Based Self-Triggered Model Predictive Control for Autonomous Underwater Vehicles with Additional Disturbances

Most existing model predictive control (MPC) methods overlook the network resource limitations of autonomous underwater vehicles (AUVs), limiting their applicability in real systems. This article addresses this gap by introducing an adaptive transmission, interval-based, and self-triggered model predictive control for AUVs operating under ocean disturbances. This approach enhances system stability while reducing resource consumption by optimizing MPC update frequencies and communication resource usage. Firstly, the method evaluates the discrepancy between system states at sampling instants and their optimal predictions. This significantly reduces the conservatism in the state-tracking errors caused by ocean disturbances compared to traditional approaches. Secondly, a self-triggering mechanism was employed, limiting information exchange to specified triggering instants to conserve communication resources more effectively. Lastly, by designing a robust terminal region and optimizing parameters, the recursive feasibility of the optimization problem is ensured, thereby maintaining the stability of the closed-loop system. The simulation results illustrate the efficacy of the controller.

Shared Decision-Making for Patients with Stroke in Neurocritical Care: A Qualitative Meta-Synthesis.

Decision-making for patients with stroke in neurocritical care is uniquely challenging because of the gravity and high preference sensitivity of these decisions. Shared decision-making (SDM) is recommended to align decisions with patient values. However, limited evidence exists on the experiences and perceptions of key stakeholders involved in SDM for neurocritical patients with stroke. This review aims to address this gap by providing a comprehensive analysis of the experiences and perspectives of those involved in SDM for neurocritical stroke care to inform best practices in this context. A qualitative meta-synthesis was conducted following the methodological guidelines of the Joanna Briggs Institute (JBI), using the thematic synthesis approach outlined by Thomas and Harden. Database searches covered PubMed, CIHAHL, EMBASE, PsycINFO, and Web of Science from inception to July 2023, supplemented by manual searches. After screening, quality appraisal was performed using the JBI Appraisal Checklist. Data analysis comprised line-by-line coding, development of descriptive themes, and creation of analytical themes using NVivo 12 software. The initial search yielded 7,492 articles, with 94 undergoing full-text screening. Eighteen articles from five countries, published between 2010 and 2023, were included in the meta-synthesis. These studies focused on the SDM process, covering life-sustaining treatments (LSTs), palliative care, and end-of-life care, with LST decisions being most common. Four analytical themes, encompassing ten descriptive themes, emerged: prognostic uncertainty, multifaceted balancing act, tripartite role dynamics and information exchange, and influences of sociocultural context. These themes form the basis for a conceptual model offering deeper insights into the essential elements, relationships, and behaviors that characterize SDM in neurocritical care. This meta-synthesis of 18 primary studies offers a higher-order interpretation and an emerging conceptual understanding of SDM in neurocritical care, with implications for practice and further research. The complex role dynamics among SDM stakeholders require careful consideration, highlighting the need for stroke-specific communication strategies. Expanding the evidence base across diverse sociocultural settings is critical to enhance the understanding of SDM in neurocritical patients with stroke.Trial registration This study is registered with PROSPERO under the registration number CRD42023461608.

The anatomy of a data transfer agreement for health research.

In a data-driven era, the exchange and safeguarding of personal information has become paramount. Data transfer agreements (DTAs) serve to guard privacy, defining the rules for sharing and protecting sensitive data. Yet, the complexities surrounding issues such as data privacy, intellectual property, and dispute resolution within these agreements pose challenges that demand careful consideration. Through a scoping review of twenty-four publicly available, English language DTAs relevant to health research, this article undertakes a comprehensive analysis, examining common clauses, their vital components, and charting a course for responsible data sharing through the provision of insights and practical guidance for drafting DTAs. The article underscores the need for attention to detail and an understanding of data protection legislation in order to ensure that DTAs align with the law and maximize legal certainty.

Analysis of Laptop Purchase History to Determine Key Factors Using Logistic Regression

E-Commerce is a new concept commonly described as the process of buying and selling goods or services over the World Wide Web or the exchange of products, services, and information through information networks, including the internet. Tokopedia, one of the largest e-commerce platforms in Indonesia, markets many products, including electronics. This study analyzes laptop data from Tokopedia. The vast history of purchase data and customer data in Tokopedia's database offers various ways to process it. One approach is using logistic regression models. The logistic regression results on the three features (price_cat, memory_size, and merk_cat) indicate that all three can be used to create logistic regression equations to determine laptop purchase probability. However, the regression calculations show that the r^2 value for price_cat is the highest at 0.82, indicating that price_cat significantly influences the probability of purchasing a laptop.

Open-Source Data Formalization through Model-Based Systems Engineering for Concurrent Preliminary Design of CubeSats

Market trends in the space sector suggest a notable increase in satellite operations and market value for the coming decade. In parallel, there has been a shift in the industrial and academic sectors from traditional Document-Based System Engineering to Model-based systems engineering (MBSE) combined with Concurrent engineering (CE) practices. Due to growing demands, the drivers behind this change have been the need for quicker and more cost-effective design processes. A key challenge in this transition remains to determine how to effectively formalize and exchange data during all design stages and across all discipline-specific tools; as representing systems through models can be a complex endeavor. For instance, during the Preliminary design (PD) phase, the integration of system models with external mathematical models for simulations, analyses, and system budgeting is crucial. The introduction of CubeSats and their standard has partly addressed the question of standardization and has aided in reducing overall development time and costs in the space sector. Nevertheless, questions about how to successfully exchange data endure. This paper focuses on formalizing a CubeSat model for use across various stages of the PD phase. The entire process is conducted with the exclusive use of open-source tools, to facilitate the transparency of data integration across the PD phases, and the overall life cycle of a CubeSat. The paper has two primary outcomes: (i) developing a generic CubeSat model using Systems modeling language (SysML) that includes data storage and visualization through the application of Unified modeling language (UML) stereotypes, streamlining in parallel information exchange for integration with various simulation and analysis tools; (ii) creating an end-to-end use case scenario within the Nanostar software suite (NSS), an open-source framework designed to streamline data exchange across different software during CE sessions. A case study from a theoretical academic space mission concept is presented as the illustration of how to utilize the proposed formalization, and it serves as well as a preliminary validation of the proposed formalization. The proposed formalization positions the CubeSat SysML model as the central data source throughout the design process. It also supports automated trade-off analyses by combining the benefits of SysML with effective data instantiating across all PD study phases.

Hybrid Text Embedding and Evolutionary Algorithm Approach for Topic Clustering in Online Discussion Forums

Leveraging discussion forums as a medium for information exchange has led to a surge in data, making topic clustering in these platforms essential for understanding user interests, preferences, and concerns. This study introduces an innovative methodology for topic clustering by combining text embedding techniques—Latent Dirichlet Allocation (LDA) and BERT—trained on a singular autoencoder. Additionally, it proposes an amalgamation of K-Means and Genetic Algorithms for clustering topics within triadic discussion forum threads. The proposed technique begins with a preprocessing stage to clean and tokenize textual data, which is then transformed into a vector representation using the hybrid text embedding method. Subsequently, the K-Means algorithm clusters these vectorized data points, and Genetic Algorithms optimize the parameters of the K-Means clustering. We assess the efficacy of our approach by computing cosine similarities between topics and comparing performance against coherence and graph visualization. The results confirm that the hybrid text embedding methodology, coupled with evolutionary algorithms, enhances the quality of topic clustering across various discussion forum themes. This investigation contributes significantly to the development of effective methods for clustering discussion forums, with potential applications in diverse domains, including social media analysis, online education, and customer response analysis.

Probabilistic Programming for Transportable Source Characterization and Uncertainty Quantification of the North Korean Nuclear Tests 2006–2017

Abstract We introduce a transportable technique to determine the yield and depth of burial (DOB) from seismic source spectra of underground nuclear explosions. We demonstrate this technique on the six declared North Korean nuclear tests. This approach derives source spectra in absolute units from regional phase (Pg) amplitudes by correcting the observations for geometric spreading, attenuation, and site amplification. We couple the source spectra and explosion source models with a probabilistic programming framework that integrates deep learning techniques and Bayesian modeling. This approach permits the exchange of information across various data categories to quantify both the data and model uncertainty. This technique stands out as an innovative use of broad-area propagation models, making it transportable across various geologic settings. This method proves to be effective in scenarios with diverse and/or limited observational data, even when the source depth is unknown. We present new independent estimates of absolute yield and DOB that are consistent with the prior assessments, underscoring the potential of this method in enhancing transportable nuclear explosion monitoring capabilities.

Nucleated synthetic cells with genetically driven intercompartment communication

Eukaryotic cells are characterized by multiple chemically distinct compartments, one of the most notable being the nucleus. Within these compartments, there is a continuous exchange of information, chemicals, and signaling molecules, essential for coordinating and regulating cellular activities. One of the main goals of bottom-up synthetic biology is to enhance the complexity of synthetic cells by establishing functional compartmentalization. There is a need to mimic autonomous signaling between compartments, which in living cells, is often regulated at the genetic level within the nucleus. This advancement is key to unlocking the potential of synthetic cells as cell models and as microdevices in biotechnology. However, a technological bottleneck exists preventing the creation of synthetic cells with a defined nucleus-like compartment capable of genetically programmed intercompartment signaling events. Here, we present an approach for creating synthetic cells with distinct nucleus-like compartments that can encapsulate different biochemical mixtures in discrete compartments. Our system enables in situ protein expression of membrane proteins, enabling autonomous chemical communication between nuclear and cytoplasmic compartments, leading to downstream activation of enzymatic pathways within the cell.

GINet:Graph interactive network with semantic-guided spatial refinement for salient object detection in optical remote sensing images

There are many challenging scenarios in the task of salient object detection in optical remote sensing images (RSIs), such as various scales and irregular shapes of salient objects, cluttered backgrounds, etc. Therefore, it is difficult to directly apply saliency models targeting natural scene images to optical RSIs. Besides, existing models often do not give sufficient exploration for the potential relationship of different salient objects or different parts of the salient object. In this paper, we propose a graph interaction network (i.e. GINet) with semantic-guided spatial refinement to conduct salient object detection in optical RSIs. The key advantages of GINet lie in two points. Firstly, the graph interactive reasoning (GIR) module conducts information exchange of different-level features via the graph interaction operation, and enhances features along spatial and channel dimensions via the graph reasoning operation. Secondly, we designed the global content-aware refinement (GCR) module, which incorporates the foreground and background feature-based local information and the semantic feature-based global information simultaneously. Experiments results on two public optical RSIs datasets clearly show the effectiveness and superiority of the proposed GINet when compared with the state-of-the-art models.