Communication Protocols Research Articles

Hybrid bidirectional quantum communication with different levels of control with simulation

In this paper, we develop a quantum communication protocol for the simultaneous preparation of a two-qubit and a three-qubit state at the positions of two different parties situated spatially apart. For one party, Alice, it is a remote state preparation of a known two-qubit state while for the other party, Bob, it is a joint remote state preparation with the help of a third party, Eve. The protocol is executed in a hybrid form bi-directionally in the presence of two controllers, Charlie and David. There is a hierarchy in the process through different levels of control under which the actions by Alice and Bob are performed. There is a need for a ten-qubit entangled channel connecting the five parties. The generation of this channel through a circuit is discussed. The protocol is executed on the IBM Quantum platform. We also study the effect of noise on our protocol. Here, amplitude-damping, bit-flip and phase-flip noisy environments are considered and the corresponding variations of fidelity are theoretically and numerically analyzed.

Unpacking the Role of Interdisciplinary Collaboration in Surgical Teams: A Critical Analysis of Operational Efficiency and Patient Outcomes

Interdisciplinary collaboration is essential for effective surgical operations, impacting both operational efficiency and patient outcomes. This critical analysis explores the dynamics of teamwork within surgical teams, focusing on how structured collaboration can streamline procedures, enhance resource management, and reduce surgical errors. Drawing from existing literature and recent empirical data, the article examines key factors that contribute to successful interdisciplinary collaboration, including role clarity, communication protocols, and mutual respect among team members. Findings reveal that well-coordinated interdisciplinary efforts lead to improved patient safety, reduced postoperative complications, and increased patient satisfaction. However, barriers such as hierarchical structures and communication breakdowns remain significant challenges. This analysis highlights actionable recommendations for fostering effective collaboration in surgical settings, such as targeted training, standardized communication strategies, and clear role definitions. Ultimately, strengthening teamwork within surgical teams not only optimizes operational efficiency but also enhances patient care quality, underscoring the importance of collaborative practices in modern healthcare.

Emotional resilience and sense of danger among doctors in hospitals during periods of heightened tensions and warfare in Israel.

The "Iron Swords" War beginning in October 2023 led to unprecedented levels of shock and trauma across Israel, significantly impacting the Israeli population and medical personnel. This study aimed to evaluate and compare the perceived personal resilience and sense of danger among physicians in hospitals located in different conflict zone proximities in Israel during this period. A quantitative, cross-sectional study was conducted from March to August 2024, during an active phase of the war, using a structured anonymous questionnaire. Participants were 161 physicians from three hospitals: one in southern Israel near the Gaza Strip, another in northern Israel near the borders with Lebanon, Syria, and Jordan, and a third in central Israel near Tel Aviv. The survey measured personal resilience using the Connor-Davidson Resilience Scale (CD-RISC-10) and sense of danger using the Solomon & Prager scale. Statistical analyses included Kruskal-Wallis H test, multiple linear regression, two-way analysis of variance (ANOVA), and Sobel test for mediation effects. The final sample included 161 physicians (54 southern, 56 central, 51 northern). The mean resilience score was 31.14 ± 5.77, and the mean sense of danger score was 8.36 ± 4.15 (scales 0-40 and 0-20, respectively). Physicians in the southern hospital reported significantly higher sense of danger scores (p = 0.005). A trend towards lower resilience scores was noted among southern hospital physicians (p = 0.068) (p = 0.068). Two-way ANOVA revealed significant main effects of hospital location and gender on resilience (p = 0.046 and p = 0.003, respectively) and sense of danger (p = 0.005 and p = 0.062, respectively). Multiple regression analysis identified hospital location (β = -0.178, p = 0.023) and gender (β = 0.229, p = 0.004) as significant predictors of resilience. Mediation analysis indicated that personal resilience partially mediated the relationship between hospital location and sense of danger (indirect effect = 0.2896, p < 0.001). Physicians working near conflict zones report higher levels of perceived danger, though their resilience is comparable to peers in less threatened regions. Enhancing personal resilience is crucial to mitigate the heightened sense of danger. This could include regular resilience training, psychological support, and specific programs for single and childless physicians to improve safety perceptions. Additionally, fostering a supportive community with clear communication and robust emergency protocols is essential for enhancing staff resilience and safety in hospitals.

Patient Safety and Medical Error Prevention in General Practice: A Comprehensive Systematic Review

Patient safety and medical error prevention remain critical issues in general practice, where medical errors can lead to adverse patient outcomes and increased healthcare costs. This systematic review synthesizes recent evidence on prevalent types of medical errors, effective prevention strategies, and outcomes of patient safety interventions within general practice settings. Following a comprehensive search across multiple databases, 52 studies published from 2014 to 2024 were included. Findings highlight medication errors, diagnostic inaccuracies, and communication lapses as the most common types of errors. Effective prevention strategies include the adoption of electronic health records (EHR), structured team communication protocols, checklist systems, and staff training programs on safety practices. These interventions have shown significant reductions in error rates and improvements in patient outcomes, though implementation is often hindered by barriers such as limited resources, resistance to change, and lack of training. This review underscores the importance of a multifaceted approach that combines technological, organizational, and educational interventions to enhance patient safety in general practice. Future research should explore long-term impacts of these interventions and address context-specific challenges to foster a culture of safety in healthcare settings.

Federated Learning : A Paradigm Shift in Collaborative Machine Learning

Federated learning (FL) has emerged as an exceptionally promising method within the realm of machine learning, enabling multiple entities to jointly train a global model while maintaining decentralized data. This paper presents a comprehensive review of federated learning methodologies, applications, and challenges. We begin by elucidating the fundamental concepts underlying FL, including federated optimization algorithms, communication protocols, and privacy-preserving techniques. Subsequently, we delve into various domains where FL has found significant traction, examples include healthcare, finance, and the Internet of Things (IoT), showcasing successful deployments and innovative strategies. Furthermore, we discuss the inherent challenges associated with federated learning, such as communication overhead, heterogeneity of data sources, and privacy concerns, and explore state- of-the-art solutions proposed in literature. Finally, we outline future research directions in federated learning, including advancements in privacy-preserving techniques, scalability improvements, and extension of FL to emerging domains. This thorough examination provides a valuable asset for researchers, practitioners, and policymakers keen on grasping the panorama of federated learning and its ramifications for collaborative machine learning in dispersed settings.

Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum

This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a multi-drone framework that leverages QRNG technology to generate truly random frequency hopping sequences, significantly improving resistance against jamming and interception attempts. Our method introduces a concurrent access protocol for multiple drones to share a QRNG device efficiently, incorporating robust error handling and a shared memory system for random number distribution. The implementation includes secure communication protocols, ensuring data integrity and confidentiality through encryption and Hash-based Message Authentication Code (HMAC) verification. We demonstrate the system’s effectiveness through comprehensive simulations and statistical analyses, including spectral density, frequency distribution, and autocorrelation studies of the generated frequency sequences. The results show a significant enhancement in the unpredictability and uniformity of frequency distributions compared to traditional pseudo-random number generator-based approaches. Specifically, the frequency distributions of the drones exhibited a relatively uniform spread across the available spectrum, with minimal discernible patterns in the frequency sequences, indicating high unpredictability. Autocorrelation analyses revealed a sharp peak at zero lag and linear decrease to zero values for other lags, confirming a general absence of periodicity or predictability in the sequences, which enhances resistance to predictive attacks. Spectral analysis confirmed a relatively flat power spectral density across frequencies, characteristic of truly random sequences, thereby minimizing vulnerabilities to spectral-based jamming. Statistical tests, including Chi-squared and Kolmogorov-Smirnov, further confirm the unpredictability of the frequency sequences generated by QRNG, supporting enhanced security measures against predictive attacks. While some short-term correlations were observed, suggesting areas for improvement in QRNG technology, the overall findings confirm the potential of QRNG-based FHSS systems in significantly improving the security and reliability of drone communications. This work contributes to the growing field of quantum-enhanced wireless communications, offering substantial advancements in security and reliability for drone operations. The proposed system has potential applications in military, emergency response, and secure commercial drone operations, where enhanced communication security is paramount.

Quantum intrusion detection system using outlier analysis

In the field of cybersecurity, hackers often enter computer systems despite current security measures, owing to the huge amount of network traffic that makes intruder identification difficult. Differentiating between authorized traffic and abnormal data produced by Distributed Denial of Service (DDoS) attackers is still a major difficulty. This research provides a unique technique that uses Quantum Machine Learning (QML) to improve security protocols for secure communication between two parties. Our strategy enhances detection accuracy and speed by using the characteristics of quantum neural networks. The approach includes creating a dataset from network traffic patterns, preprocessing it, and then turning it into quantum bits via angle embedding. The research uses outlier analysis, min-entropy, and quantum state fidelity to distinguish between normal and abnormal data patterns. The dispersed randomness of network header data is measured using entropy, which aids in identifying security concerns. The suggested QML-based methodology outperforms conventional approaches and current models, including AMM-CNN and ANN models, with a detection accuracy of 99.87% for DDoS attacks. This advancement leads to more effective and secure communication networks.

Message Action Adapter Framework in Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) has demonstrated significant potential in enabling cooperative agents. The communication protocol, which is responsible for message exchange between agents, is crucial in cooperation. However, communicative MARL systems still face challenges due to the noisy messages in complex multi-agent decision processes. This issue often stems from the entangled representation of observations and messages in policy networks. To address this, we propose the Message Action Adapter Framework (MAAF), which first trains individual agents without message inputs and then adapts a residual action based on message components. This separation isolates the effect of messages on action inference. We explore how training the MAAF framework with model-agnostic message types and varying optimization strategies influences adaptation performance. The experimental results indicate that MAAF achieves competitive performance across multiple baselines despite utilizing only half of the available communication, and shows an average improvement of 7.6% over the full attention-based communication approach. Additional findings suggest that different message types result in significant performance variations, emphasizing the importance of environment-specific message types. We demonstrate how the proposed architecture separates communication channels, effectively isolating message contributions.

Passive and Active Exoskeleton Solutions: Sensors, Actuators, Applications, and Recent Trends.

Recent advancements in exoskeleton technology, both passive and active, are driven by the need to enhance human capabilities across various industries as well as the need to provide increased safety for the human worker. This review paper examines the sensors, actuators, mechanisms, design, and applications of passive and active exoskeletons, providing an in-depth analysis of various exoskeleton technologies. The main scope of this paper is to examine the recent developments in the exoskeleton developments and their applications in different fields and identify research opportunities in this field. The paper examines the exoskeletons used in various industries as well as research-level prototypes of both active and passive types. Further, it examines the commonly used sensors and actuators with their advantages and disadvantages applicable to different types of exoskeletons. Communication protocols used in different exoskeletons are also discussed with the challenges faced.

Bridging IoT Protocols with the Web of Things: A Path to Enhanced Interoperability

Nowadays, the Internet of Things (IoT) is reshaping numerous application domains. Amidst the different communication protocols currently available (MQTT, CoAP, AMQP, DPWS, etc.) and the abundance of management platforms, the IoT domain has stumbled into vertical silos of proprietary systems hindering interoperability. Within this intricate and fragmented ecosystem, the need for an intelligent and scalable architecture promoting interoperability becomes imperative to maximize the potential of IoT. This paper introduces a system designed to enhance the convergence of IoT protocols (e.g., MQTT, CoAP, AMQP, etc.) with the Web of Things (WoT) paradigm. Our proposed gateway-based solution integrates two systems: Stack4Things (S4T) and Data eXchange Mediator Synthesizer (DeXMS). The role of the latter is to adapt, at the gateway level, the IoT protocols and expose the resources/functionalities of the IoT devices as RESTful APIs using HTTP. Meanwhile, the former (i.e., S4T), leveraging its Dynamic DNS system, ensures that these RESTful resources (exposed at the gateway) are accessible over the Web using publicly routable Uniform Resource Locators (URLs) even when the gateway is deployed behind networking middle boxes (e.g., NATs and firewalls).

Petri net modeling and analysis of an IoT‐enabled system for real‐time monitoring of eggplants

AbstractAs the current agricultural industry is facing several challenges such as climate changes and lack of qualified farmers, it is extremely necessary to ensure sustainable agriculture and food supply by smart farming (SF). The SF can assist farmers and the associated stakeholders in making correct decisions on improving the yield and quality of agricultural products. In this study, an SF system based on low‐cost Raspberry Pi and Internet of Things (IoT) technologies has been successfully implemented. Through the data items detected by sensors to deeply manage the planting process, the IoT‐enabled communication protocol under ISO standards of Message Queuing Telemetry Transport (MQTT) was used. The bar charts of real‐time environmental parameters are presented on ThingsBoard to achieve the goal of data visualization. Meanwhile, a web server is built for the customized requirements, and the historical datasets are stored in the SQLite database system. Furthermore, a Petri net (PN) model was employed to detect all possible abnormal processes and to verify the feasibility and soundness of an IoT‐enabled system by using a software tool, WoPeD. Finally, the experimental results show that the plants cultivated by the proposed IoT‐enabled system are superior to those cultivated by the existing systems in many perspectives, including the monitoring distance, power consumption, precision, deadlock detection, and running time.

The Power of a Good Word: Enhancing the Efficacy of Analgesics in Clinical Settings

Introduction: Communication between medical staff and patients about treatment efficacy elicits expectations of benefit and improves treatment outcomes. While demonstrated in multiple studies via different research methodologies, uniform communication protocols have not been adopted in clinical practice. Here, we summarize the results of two sister studies aimed at bridging this gap. Methods: Women undergoing C-section (study 1, randomized controlled trial) and patients undergoing general or otolaryngologic surgeries (study 2, control group design) were recruited and assigned to the “regular communication” (RC) or “enhanced communication” (EC) arms. The EC arm received positive information about treatment, while the RC arm received no such information. In both studies, the primary outcome was change in pain intensity; in study 2, an additional outcome was morphine consumption. Results: Eighty women successfully completed study 1, and 102 patients successfully completed study 2. In both studies, significant time*group interactions were observed (p < 0.001). The analgesic effect was virtually twice as large in the EC arm compared to the RC arm. In study 2, in the last two timepoints of assessment, participants in the EC arm also consumed fewer doses of opioids than participants in the RC arm (p < 0.001). No significant differences were found in vital signs. Conclusions: We provide ecological evidence that positive information about treatment significantly decreases pain and opioid consumption during routine clinical care. This study and others could encourage healthcare providers to harness the powerful effects of patients’ expectations of benefit to improve analgesics outcomes and, potentially, the outcomes of other symptoms.

High‐Fidelity Information Transmission Through the Turbulent Atmosphere Utilizing Partially Coherent Cylindrical Vector Beams

As the demand for high‐capacity and high‐fidelity communication systems continues to increase, addressing the challenges posed by noise and atmospheric turbulence disturbances is imperative. This study introduces and experimentally implements a novel free‐space optical communication protocol. This protocol combines the advantages of reducing the spatial coherence of light at the source with the capabilities of convolutional neural networks at the receiver to encode and transmit optical images through a noisy link. Light beams that are robust against noise are generated and atmospheric turbulence is modeled in a laboratory setting by decreasing the degree of spatial coherence of the source. Eight orbital angular momentum states, four polarizations, and eight coherence states of a light source that generates partially coherent cylindrical vector beams are utilized. These elements are employed to achieve a 256‐ary encoding/decoding data transmission within our protocol. This study is expected to catalyze further research into the utilization of partially coherent light and neural networks in the realm of free‐space optical communications.

RTPL: A Real-Time Communication Protocol for LoRa Network

The industrial Internet of Things (IIoT) is prominently emerging in applications of large-scale and wide-area applications, such as oilfield management, smart grid management, real-time equipment monitoring, and integration of traffic management systems for smart cities. Relying on short-range wireless technologies (e.g., WirelessHART and ISA100.11a), traditional wireless solutions for industrial automation find it challenging to support the expansive scale of today’s IIoT. To address this limitation, we propose to adopt LoRaWAN, a prominent low-power wide-area network technology, for industrial automation. LoRaWAN for industrial automation poses some unique challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. LoRaWAN traditionally adopts a simple protocol based on ALOHA with no collision avoidance or Listen Before Talk with Clear Channel Assessment and Random Backoff mechanisms to minimize energy consumption, which are less suitable for real-time communication. Existing real-time protocols for short-range technologies cannot be applied to a LoRaWAN network due to its unique characteristics such as asymmetry between downlink and the uplink spectrum, predefined modes (or classes) of operation, and concurrent reception through orthogonal spreading factors. In this paper, we address these challenges and propose RTPL - a Real-Time communication Protocol for LoRaWAN networks. RTPL is a low-overhead and conflict-free communication protocol allowing autonomous real-time communication of low-energy devices and exploits LoRa’s capability of parallel communication. We implement our approach on LoRa devices and evaluate through both physical experiments and extensive simulations. All results show that RTPL achieves on average 75% improvement in real-time performance without sacrificing throughput or energy compared to traditional LoRaWAN.

Spatiotemporal Moisture Field

For monitoring capillary moisture conduction, the most important parameter is the moisture conductivity coefficient, which is a material characteristic; however, its use in practical calculations is not very common. For further development in the field of liquid moisture propagation, an automated measuring apparatus has been developed and granted a European patent. Its essence lies in detecting the liquid water content based on a well-known physical phenomenon: electromagnetic radiation in the microwave range. The determination of the spatiotemporal moisture field is the first and fundamental step for describing transportation phenomena. The moisture field thus created allows for the viewing of the moisture conductivity coefficient, which is one of the most important parameters in describing transportation phenomena as a function of moisture. The presence of water in building materials can significantly affect their physical properties, such as mechanical or thermal–technical characteristics. This may lead to unacceptable consequences, which might only manifest after a certain period of time. In the case of multi-layered structures, moisture can transfer from one material to another. Therefore, it is essential to address this process. The advantage of the software solution described by the methodology is the use of an open communication protocol in the form of a synchronized array, which is not common in typical applications of this type. The principle of separating hardware modules is also unusual for devices of this type, as it requires the independent communication of each module with the control software. Mutual communication is handled exclusively at the software level, making it possible to modify, optimize, or parameterize the procedures as needed. Upon closer examination of the wetting curves of various materials, anomalies were revealed in some of their structures. This can be advantageously utilized in the research of newly developed composite materials. The assembled system of measuring instruments, their software integration, and control provide a foundation for the practical application of the described procedures and methods for determining the moisture field of building materials. The parameterization of individual processes, as well as the open access to data, allows for the optimization of the methodology, as materials of entirely different characteristics may require an individual approach, which will certainly contribute to the advancement of science and research in this area. Currently, this work is being followed by further extensive studies, not yet published by the authors, focusing on the application of the described moisture field to evaluate the moisture conductivity coefficient as a function dependent on the material’s mass moisture content. Their application requires specific mathematical and programming approaches due to the significant volume of data involved.

Autonomous Vehicle Data Protection : A Review of Security Threats, Challenges and Protective Mechanism

The swift advancement of autonomous vehicle (AV) technology has presented notable obstacles with data security and privacy. This analysis examines major security threats that jeopardize user privacy and vehicle control, including data breaches, virus penetration, cyber attacks, illegal access, V2V and V2I system hacking, GPS spoofing, and data leaks. It also looks at the challenges developers have when putting strong security measures in place, considering how complicated AV infrastructures may be and how cloud, IoT, and AI are integrated. The paper outlines a number of defence tactics, such as blockchain technology, machine learning for anomaly detection, encryption, and secure communication protocols. In order to improve AV cyber security, it ends with suggestions for further study in fields like edge computing and quantum-resistant cryptography.

Global Well-Posedness and Refined Regularity Criterion for the Uni-Directional Euler-Alignment System

Abstract We investigate global solutions to the Euler-alignment system in $d$ dimensions with unidirectional flows and strongly singular communication protocols $\phi (x) = |x|^{-(d+\alpha )}$ for $\alpha \in (0,2)$. Our paper establishes global regularity results in both the subcritical regime $1&amp;lt;\alpha &amp;lt;2$ and the critical regime $\alpha =1$. Notably, when $\alpha =1$, the system exhibits a critical scaling similar to the critical quasi-geostrophic equation. To achieve global well-posedness, we employ a novel method based on propagating the modulus of continuity. Our approach introduces the concept of simultaneously propagating multiple moduli of continuity, which allows us to effectively handle the system of two equations with critical scaling. Additionally, we improve the regularity criteria for solutions to this system in the supercritical regime $0&amp;lt;\alpha &amp;lt;1$.

Linguistic sensitivity in family therapy: A review of language adaptation strategies for enhancing therapeutic engagement

Language plays a pivotal role in family therapy, shaping the therapeutic process and influencing client engagement and outcomes. This review explores the significance of linguistic sensitivity in family therapy and examines various language adaptation strategies aimed at enhancing therapeutic engagement. Drawing upon existing literature, this paper synthesizes key findings and offers insights into effective approaches for addressing linguistic diversity within therapeutic contexts. Understanding the cultural and linguistic backgrounds of clients is essential for promoting inclusivity and facilitating effective communication in family therapy. Cultural competence encompasses not only an awareness of diverse cultural norms and values but also proficiency in navigating linguistic nuances. By recognizing the impact of language on therapeutic interactions, therapists can foster a more inclusive and empowering therapeutic environment. The review identifies several language adaptation strategies that therapists can employ to enhance therapeutic engagement with diverse client populations. These strategies include linguistic matching, which involves adjusting language patterns and communication styles to resonate with clients' linguistic preferences. Additionally, the use of culturally relevant metaphors, idioms, and storytelling techniques can facilitate deeper rapport and understanding between therapists and clients. Furthermore, the review discusses the importance of employing interpreters and bilingual therapists to bridge language barriers effectively. It emphasizes the need for clear communication protocols and the importance of training therapists in language adaptation techniques to ensure ethical and effective practice. Moreover, the review highlights the role of technology in language adaptation, such as the use of translation software and teletherapy platforms with multilingual capabilities. Leveraging technology can broaden access to therapy and support linguistic diversity in therapeutic settings. Linguistic sensitivity is fundamental to promoting inclusivity and effectiveness in family therapy. By adopting language adaptation strategies and embracing linguistic diversity, therapists can cultivate a more responsive and culturally competent approach to working with diverse families. This review underscores the importance of ongoing research and training initiatives to further enhance linguistic sensitivity within the field of family therapy.

Hybrid quantum communication protocol for fiber and atmosphere channel

Hybrid quantum communication protocol for fiber and atmosphere channel

Edge-Driven Linux Platform for Power Quality Solutions

In the modern landscape of data centers, ensuring optimal power quality and efficiency is paramount. This paper describes designing and implementing an edge-driven Linux platform for power quality solutions for real-time measurement, analysis, and conformity with IEC 61000-4-30, IEEE 519, and EN 50160 standards. It employs an AM6442xx processor and has a web interface with the backing of an efficient embedded web server, a NodeJS, and a Nginx server. It supports industrial communication protocols like Modbus TCP to simplify integration into existing systems. In addition, it involves risk assessment, threat modeling, and enhanced ability of the platform to withstand tests that threaten the confidentiality and integrity of data. Thus, the proposed solution increases the efficiency of data center operations and provides reliable power quality metering that meets current international standards.