Search

We propose a novel bundled-path-routing node architecture for multi-band optical networks and a network design algorithm based on graph degeneration. Feasibility is demonstrated through experiments on a prototype with 300.8 Tbps throughput.

Objectives: This review aimed at investigating the impact of bioactive agents on alveolar ridge augmentation at peri-implant sites and edentulous ridges. Material and Methods: Electronic and manual literature searches were conducted for articles up to April 2021. Human randomized clinical trials, clinical trials, or retrospective studies reporting outcomes of alveolar ridge augmentation procedures with or without bioactive agents were included. Sinus lifting or extraction socket preservation articles were excluded in this study. A random-effect meta-analysis was conducted to investigate ridge dimensional changes and complication rate. Results: Overall, ten articles were included in the quantitative analysis. In bone width and height augmentation, the weighted mean difference (WMD) of the horizontal bone gain at edentulous ridges was 0.38 mm (95% CI = 0.07 to 0.69 mm, p = 0.02) when comparing sites with and without PRP/PRF. The WMD of the vertical bone gain at edentulous ridges was 0.46 mm (95% CI = 0.06 to 0.86 mm, p = 0.02) when comparing sites with and without platelet- rich plasma (PRP) or platelet-rich fibrin (PRF). When evaluating the effect of adjunctive use of bone morphogenetic proteins (BMPs) at alveolar ridge augmentation sites compared to alveolar ridge augmentation sites without BMP application, the WMD of the horizontal bone gain at edentulous ridges was 0.90 mm (95% CI = 0.30 to 1.49 mm, p = 0.003). In addition, the odds ratio of the titanium mesh exposure was 0.13 (95% CI of 0.02 to 0.71, p = 0.02). All analyses showed favorable outcomes at the sites with bioactive agent. Conclusion: Within the limits of this review, application of bioactive agents during alveolar ridge augmentation procedures presents increased horizontal and vertical bone augmentative changes. The complication rate can also be reduced with usage of PRP/PRF in titanium mesh-guided alveolar ridge augmentation. &nbsp

Background: Understanding the conceptual models that underpin interventions, and the linkage between mechanisms of action and their intended outcomes, makes replication possible. Aim: To identify and appraise conceptual models and mechanisms of action underpinning end-of-life care interventions to improve spiritual well-being. Design: A systematic review following the Preferred Reporting Items for Systematic Review and Meta-Analysis was conducted. Data sources: A comprehensive search was performed in eight databases from inception to January 12, 2021. Results: A logic model was developed and potential mechanisms of action were identified from the seven included studies. Conclusion: First, conceptual models that have relevance and appropriateness to cultural setting are required to underpin future intervention development and implementation. Second, careful intervention development should articulate the link between concept, mechanisms, and outcomes. Third, selection of valid outcome measured must have a strong justification of how the construct being measured relates to the intervention goals.

We characterize the capacity region of the two-user broadcast packet erasure channel (PEC) with single-user delayed channel state information (CSI). More precisely, we assume one receiver does not provide its channel state to the other two nodes (the other receiver and the transmitter), while the other receiver reveals its state globally with unit delay. This is a hybrid CSI at the transmitter (CSIT) setting where the transmitter has the delayed CSI of one user but not the other. Previous results developed opportunistic network coding schemes for this setting, which strictly enlarge the achievable rate region compared to the no-CSIT baseline. Characterization of the capacity region with single-user delayed CSI, however, remained open. In this work, we develop an improved achievability strategy and show that the capacity region, surprisingly, matches that of the broadcast PEC with global delayed CSI of both users. The key to such improvement over previous results is a new precoding strategy for the retransmission phase of the opportunistic network coding scheme. It harnesses the single-user delayed CSI in the retransmission phase, so that interference from the feedback receiver can be aligned at the other receiver. Besides the broadcast PEC with two private messages, an extension to a model with an additional common message is also provided and the corresponding capacity region with single-user CSI also matches that with global delayed CSI. Finally, further extensions to three-user cases are also provided.

Smart manufacturing based on artificial intelligence and information communication technology will become the main contributor to the digital economy of the upcoming decades. In order to execute flexible production, smart manufacturing must holistically integrate wireless networking, computing, and automatic control technologies. This article discusses the challenges of this complex system engineering from a wireless networking perspective. Starting from enabling flexible reconfiguration of a smart factory, we discuss existing wireless technology and the trends of wireless networking evolution to facilitate multirobot smart factories. Furthermore, the special sequential decision-making of a multirobot manufacturing system is examined. Social learning can be used to extend the resilience of precision operation in a multirobot system by taking network topology into consideration, which also introduces a new vision for the cybersecurity of smart factories. A summary of highlights of technological opportunities for holistic facilitation of wireless networked multirobot smart factories rounds off this article.

Summary Miniaturized transponder systems are under development for monitoring unconventional reservoirs, mapping hydraulic fractures, and determining other wellbore parameters. These gadgets are an extension of radio-frequency identification (RFID) and are known as fracture robot (FracBot) nodes to recognize wireless underground sensor networks (WUSNs) for characterization and mapping of hydraulic breakages in unconventional reservoirs. 3D constellation maps of proppant-bed placement are generated by autonomous localization algorithms as FracBots are injected during hydraulic-fracturing operations. To investigate this model, a FracBot platform was established to explore this concept, and three basic functions have been explained. First, we have developed an innovative cross-layer communication model for magnetic-induction (MI) networks in altering underground environments, coupled with selections of coding, modulation, and power control and a geographic forwarding structure. Second, we have developed an innovative MI-based localization framework to capture the locations of the randomly deployed FracBot nodes by exploiting the exceptional properties of the MI field. Third, we have proposed an energy model for a linear FracBot network scheme that provides reasonable data rates while preserving collected energy limitations. Finally, to examine the functionalities of FracBot nodes in air, sand, and stone media, a physical MI-based WUSN test bed was implemented. Experiments indicated that the constructed FracBots can form a communication link and transfer data over amplitude-shift keying (ASK) modulation with 1.6 kbit/sec as a data rate and a minimum receiver sensitivity of −70 dBm. The performance of near-field-communication (NFC) antennas was affected by sand and stone media, which ultimately affect MI signal propagation and decrease the energy transfer. In sand or stone media, augmented mismatch between transmitter and receiver antennas was detected, leading to the decision that an advanced matching circuit design or an adaptive-frequency feature should be integrated into the FracBot design. This permits an optimal energy transmission and consistent communication link through sand and stone media.

ObjectiveTo determine the impact of palliative care (PC) on end-of-life (EoL) care and the place of death (PoD) in children, adolescents, and young adults with life-limiting conditions.MethodEight online databases (PubMed, Medline, EMBASE, Cochrane Library, CINAHL, Airiti, GARUDA Garba Rujukan Digital, and OpenGrey) from 2010 to February 5, 2020 were searched for studies investigating EoL care and the PoD for pediatric patients receiving and not receiving PC.ResultsOf the 6,468 citations identified, 14 cohort studies and one case series were included. An evidence base of mainly adequate- and strong-quality studies shows that inpatient hospital PC, either with or without the provision of home and community PC, was found to be associated with a decrease in intensive care use and high-intensity EoL care. Conflicting evidence was found for the association between PC and hospital admissions, length of stay in hospital, resuscitation at the time of death, and the proportion of hospital and home deaths.Significance of resultsCurrent evidence suggests that specialist, multidisciplinary involvement, and continuity of PC are required to reduce the intensity of EoL care. Careful attention should be paid to the need for a longer length of stay in a medical setting late in life, and earlier EoL care discussion should take place with patients/caregivers, especially in regard to attempting resuscitation in toddlers, adolescents, and the young adult population. A lack of robust evidence has identified a gap in rigorous multisite prospective studies utilizing data collection.

To provide coordinate information for the use of intelligent transportation systems (ITSs) and autonomous vehicles (AVs), the global positioning system (GPS) is commonly used in vehicle localization as a cheap and easily accessible solution for global positioning. However, several factors contribute to GPS errors, decreasing the safety and precision of AV and ITS applications, respectively. Extensive research has been conducted to address this problem. More specifically, several optimization-based cooperative vehicle localization algorithms have been developed to improve the localization results by exchanging information with neighboring vehicles to acquire additional information. Nevertheless, existing optimization-based algorithms still suffer from an unacceptable performance and poor scalability. In this study, we investigated the development of deep learning (DL) based cooperative vehicle localization algorithms to provide GPS refinement solutions with low complexity, high performance, and flexibility. Specifically, we propose three DL models to address the problem of interest by emphasizing the temporal and spatial correlations of the extra given information. The simulation results confirm that the developed algorithms outperform existing optimization-based algorithms in terms of refined error statistics. Moreover, a comparison of the three proposed algorithms also demonstrates that the proposed graph convolution network-based cooperative vehicle localization algorithm can effectively utilize temporal and spatial correlations in the extra information, leading to a better performance and lower training overhead.

This paper investigates the transmission energy minimization problem for the two-user downlink with strictly heterogeneous latency constraints. To cope with the latency constraints and to explicitly specify the trade-off between blocklength (latency) and reliability the normal approximation of the capacity of finite blocklength codes (FBCs) is adopted, in contrast to the classical Shannon capacity formula. We first consider the non-orthogonal multiple access (NOMA) based transmission scheme. However, due to heterogeneous latency constraints and channel conditions at receivers, the conventional successive interference cancellation may be infeasible. We thus study the problem by considering heterogeneous receiver conditions under different interference mitigation schemes and solve the corresponding NOMA design problems. It is shown that, though the energy function is not convex and does not have closed form expression, the studied NOMA problems can be globally solved semi-analytically and with low complexity. Moreover, we propose a hybrid transmission scheme that combines the time division multiple access (TDMA) and NOMA. Specifically, the hybrid scheme can judiciously perform bit and time allocation and take TDMA and NOMA as two special instances. To handle the more challenging hybrid design problem, we propose a concave approximation of the FBC rate/capacity formula, by which we obtain computationally efficient and high-quality solutions. Simulation results show that the hybrid scheme can achieve considerable transmission energy saving compared with both pure NOMA and TDMA schemes., Comment: 15 pages, 10 figures, IEEE Transactions on Wireless Communications

Low earth orbit (LEO) mega-constellations, integrating government space systems and commercial practices, have emerged as enabling technologies for the sixth generation (6G) networks due to their good merits of global coverage and ubiquitous services for military and civilian use cases. However, convergent LEO-based satellite networking infrastructures still lack leveraging the synergy of space and terrestrial systems. This paper, therefore, extends conventional serverless cloud platforms with serverless edge learning architectures for 6G proliferated LEO (p-LEO) satellite ecosystems and provides a new distributed training design from a networking perspective. The proposed design dynamically orchestrates communications and computation functionalities and resources among heterogeneous physical units to efficiently fulfill multi-agent deep reinforcement learning for service-level agreements. Innovative ecosystem enhancements, including ultrabroadband access, anti-jammed transmissions, resilient networking, and related open challenges, are also investigated for end-to-end connectivity, communications, and learning performance., Comment: Submitted for possible publication to the IEEE Communication magazine

The three-dimensional virtual world generated in virtual reality (VR) environments involves vision, hearing, touch, and potentially other user senses. VR environments are designed to closely simulate real situations/scenarios, allowing users to observe and engage in experiences free of time and space restrictions. Virtual patients are an extension of virtual reality. In recent years, virtual reality exercises have been widely used in various medical education and training programs to promote professional competence. The clinical practice of nurse practitioners is similar to physicians in some institutions. Nurse practitioners perform supplementary medical services such as physical assessment, medical history collection and consultation, problem detection, test result assessment, differential diagnosis, and patient health problem decision-making under the supervision of physicians. The critical thinking component in nurse practitioner education is largely similar to that taught in medical education. VR is often used in medical education to train medical students. Also, VR may be applied in critical thinking training for nurse practitioners to promote competence in reasoning and differential diagnosis. However, few studies in the literature have evaluated the effectiveness of VR-based training for nurse practitioners in Taiwan. In the future, this issue needs to be studied further.虛擬互動實境輔助專科護理師批判性思考訓練之應用.虛擬實境（virtual reality）是利用電腦產生一個三度空間的虛擬世界，提供使用者視覺、聽覺、觸覺等不同感官的感受，使用者感覺彷彿身歷其境，沒有時間及空間限制，可觀察及體驗所設計的三度空間情境。虛擬病人（virtual patient）是虛擬實境的延伸發展，近年來已被廣為使用多年在各種醫護教育訓練，透過虛擬實境練習，以提升專業能力。有些機構專科護理師的臨床業務與醫師範疇完全一致，是在醫師監督下執行輔助醫療業務，其中包含身體評估、病史收集及問診、探測病人異常表現、判斷檢驗結果、鑑別診斷及病人健康問題之決策等，因此專科護理師訓練與醫學教育所需要培養臨床思辨目標一致。虛擬實境已用於醫學教育來訓練醫學系學生，因此很適合使用虛擬實境的教育方式來做專科護理師批判性思考的訓練，以提升思辨及推理鑑別診斷之能力，但因目前台灣較少有針對專科護理師虛擬實境病人訓練使用及成效探討，故未來值得深入研究及推展。.

In this work, we investigate a two-user Gaussian broadcast channel (GBC) with heterogeneous blocklength constraints, specified according to users' channel output signal to noise ratios (SNRs). Unlike traditional GBC where two users have the same blocklength constraints, here the user with higher output SNR may have a shorter blocklength constraint. Then it is unclear whether this user can decode the interference to perform successive interference cancellation (SIC) or not. We argue that with sufficient large output SNR, this user can invoke the technique named as the early decoding to decode the interference. Then SIC can proceed. We derive an explicit lower bound on the necessary blocklength for successful early decoding, using an independent and identically distributed Gaussian input, given a maximal input power constraint and an average error probability constraint. A huge SNR gain can be achieved over treating interference as noise when SIC is applied with the aid of early decoding.

Motivated by the applications for low-delay communication networks, the finite-length analysis, or channel dispersion identification, of the multi-user channel is very important. Recent studies also incorporate the effects of feedback in point-to-point and common-message broadcast channels (BCs). However, with private messages and feedback, finite-length results for BCs are much more scarce. Though it is known that feedback can strictly enlarge the capacity, the ultimate feedback capacity regions remain unknown for even some classical channels including Gaussian BCs. In this work, we study the two-user broadcast packet erasure channel (PEC) with causal feedback, which is one of the cleanest feedback capacity results and the capacity region can be achieved by elegant linear network coding (LNC). We first derive a new finite-length outer bound for any LNCs and then accompanying inner bound by analyzing a three-phase LNC. For the outer-bound, we adopt a linear-space-based framework, which can successfully find the LNC capacity. However, naively applying this method in finite-length regime will result in a loose outer bound. Thus a new bounding technique based on carefully labelling each time slot according to the type of LNC transmitted is proposed. Simulation results show that the sum-rate gap between our inner and outer bounds is within 0.02 bits/channel use. Asymptotic analysis also shows that our bounds bracket the channel dispersion of LNC feedback capacity for broadcast PEC to within a factor of Q-l (E/2)/Q-l (E).

Streaming codes take a string of source symbols as input and output a string of coded symbols in real time, which effectively eliminate the queueing delay and are regarded as a promising scheme for low latency communications. Aiming at quantifying the fundamental latency performance of random linear streaming codes (RLSCs) over i.i.d. symbol erasure channels, this work derives the exact error probability under, simultaneously, the finite memory length and finite decoding deadline constraints. The result is then used to examine the tradeoff among memory length (complexity), decoding deadline (delay), and error probability (reliability) of RLSCs for the first time in the literature. Two critical observations are made: (i) Too much memory can adversely impact the performance under a finite decoding deadline constraint, a surprising finding not captured by the traditional wisdom that large memory length monotonically improves the performance in the asymptotic regime; (ii) The end-to-end delay of the RLSC is roughly 50% of that of the MDS block code when under identical code rate and error probability requirements. This implies that switching from block codes to RLSCs not only eliminates the queueing delay (thus 50%) but also has little negative impact on the error probability.

Cyber-physical systems, such as smart grids, have received lots of attention recently. Unfortunately, security breaches in cyber-physical systems can result in catastrophic consequences, thus needing to be carefully monitored. For example, abnormal voltage quality events, which are more likely to happen because of unstable renewable energy sources in smart grids, harm delicate electronic devices. We thus focus on the quickest classification, or multi-hypothesis quickest change detection, which jointly detects and classifies multiple abnormal events. Both the classification delay and misclassification probability need to be low. Multiple smart meters are adopted, where each meter transmits its local decision to a fusion center for making the final decision. For energy saving, the bandwidth (link capacity) between each meter and the fusion center is limited to be one bit. Moreover, some meters may be faulty and mislead the final decision. To combat these faulty meters under the limited bandwidth, a code-based framework for quickest classification is proposed. Our contribution is two-fold. First, a new local decision rule based on the stochastic ordering theory is proposed. Compared with existing matrix-cumulative-sums algorithm, the newly proposed local decision rule has lower complexity and comparable performance. Second, a new fusion method based on codebook switching and minimum Hamming distance rule is developed. Compared with existing fault-tolerant methods, the newly-developed method can significantly lower the misclassification probabilities.

Supporting on-line and adaptive traffic engineering in software-defined networks entails the fast, robust control message forwarding from software-defined switches to the controller(s). In-band control using the existing infrastructure is cost-efficient, but imposes a substantial barrier to timely transmissions of control messages. Also, due to the limited computational capability of a single controller, only the use of multiple controllers is practically viable for large-scaled networks. Therefore, in this paper, the optimal software-defined network planning is investigated with multi-controllers. First, the network planning problem is formulated as a nonlinear multi-objective optimization, which aims to simultaneously minimize the number of controllers and the control traffic delay for each switch. This planning problem is then partitioned into two sub-problems, i.e., multi-controller placement and control traffic balancing, which are respectively solved by the proposed fast-convergent algorithms. Furthermore, an adaptive feedback control mechanism is proposed to iteratively work out the two sub-problems and enable the dynamic network replanning, subject to the time-varying traffic volume and network topology. Simulations validate the adaptivity of our control scheme, which significantly reduces delay with maximum throughput for control flows, brings minimal impact to normal data flows, and requires the minimum controllers.

Specialist palliative care (SPC) is often needed to manage complex or refractory problems in children with life-threatening conditions during end-of-life. This study explores the perceptions of healthcare professionals (HPs) to determine the triggers leading to and experiences with introducing SPC among families of children with life-threatening conditions.A secondary analysis of 13 semi-structured interviews with HPs conducted from September 2019-June 2020 was carried out in a pediatric ward and a neonatal and pediatric intensive care unit in Taiwan. A thematic analysis was conducted. Competence Theory was used to guide the research questions and the interpretive framework.Seven nurses, four pediatricians, one psychologist, and one respiratory therapist were interviewed. The need for shared knowledge regarding wishes for care and end-of-life decision-making were found to be the indicators for introducing SPC, along with having a fear of causing harm to the family-professional relationship and the patient. HPs value harmony in the form of clarifying misconceptions, building trust, and holding the moral bottom line. The theme of 'seeking the competent self' encompasses the values and expectations related to improving skills and creating a sense of fulfillment as HPs achieve good quality care.Discussions about SPC facilitate better communication and decision-making. Careful attention should be paid to the needs related to clarifying misconceptions and protecting the child's right to life when SPC is suggested.Communication, empathy, and conflict resolution training may be helpful with developing HP competencies related to introducing SPC.

Intelligent transportation systems and autonomous vehicles are expected to bring new experiences with enhanced efficiency and safety to road users in the near future. However, an efficient and robust vehicular communication system should act as a strong backbone to offer the needed infrastructure connectivity. Deep learning (DL)-based algorithms are widely adopted recently in various vehicular communication applications due to their achieved low latency and fast reconfiguration properties. Yet, collecting actual and sufficient transportation data to train DL-based vehicular communication models is costly and complex. This paper introduces a cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access. This integrated platform can generate realistic transportation and communication data, benefiting the development and adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition and management are further investigated to demonstrate the potentials of dynamic slidelink access. Numerical results show that our platform can effectively train and realize DL-based C-V2X algorithms. The developed slidelink communication can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments.

Beamforming is a key technology in communication systems of the fifth generation and beyond. However, traditional optimization-based algorithms are often computationally prohibited from performing in a real-time manner. On the other hand, the performance of existing deep learning (DL)-based algorithms can be further improved. As an alternative, we propose an unsupervised ResNet-inspired beamforming (RI-BF) algorithm in this paper that inherits the advantages of both pure optimization-based and DL-based beamforming for efficiency. In particular, a deep unfolding technique is introduced to reference the optimization process of the gradient ascent beamforming algorithm for the design of our neural network (NN) architecture. Moreover, the proposed RI-BF has three features. First, unlike the existing DL-based beamforming method, which employs a regularization term for the loss function or an output scaling mechanism to satisfy system power constraints, a novel NN architecture is introduced in RI-BF to generate initial beamforming with a promising performance. Second, inspired by the success of residual neural network (ResNet)-based DL models, a deep unfolding module is constructed to mimic the residual block of the ResNet-based model, further improving the performance of RI-BF based on the initial beamforming. Third, the entire RI-BF is trained in an unsupervised manner; as a result, labelling efforts are unnecessary. The simulation results demonstrate that the performance and computational complexity of our RI-BF improves significantly compared to the existing DL-based and optimization-based algorithms.

5G downlink communications are expected to suffer more from the intermittent link connectivity, and studying the erasure broadcast channel (BC) provides the fundamental understanding of their performance. Recently, the capacity region of two-user erasure BC with single-user delayed channel state information (CSI) was characterized, where one receiver does not provide its channel state to the transmitter and the other receiver, while the other receiver feeds back its state globally with unit delay. Surprisingly, the capacity region with single-user delayed CSI matches that of the erasure BC with global delayed CSI of both users. When two links have same erasure probabilities, this result is valid even when a common message intended for both users is required besides two private messages. The capacity in this case is easily achieved by adding a phase multicasting the common message after the transmission of private messages. However, when erasure probabilities are unequal, this scheme is clearly not capacity-achieving since the rate of the last phase will be limited by the weaker user having larger erasure probability. We propose a scheme which transmits the common message with rate higher than the link capacity of the weaker user in the last phase but still can ensure the decodability. The key is to simultaneously create equations of the common message at the weaker receiver and re-transmit private bits before the last phase. The proposed scheme achieves the converse under unequal link erasure probabilities, and the capacity with common and private messages is fully characterized under single-user delayed CSI.

This paper introduces an energy-efficient, software-defined vehicular edge network for the growing intelligent connected transportation system. A joint user-centric virtual cell formation and resource allocation problem is investigated to bring eco-solutions at the edge. This joint problem aims to combat against the power-hungry edge nodes while maintaining assured reliability and data rate. More specifically, by prioritizing the downlink communication of dynamic eco-routing, highly mobile autonomous vehicles are served with multiple low-powered access points (APs) simultaneously for ubiquitous connectivity and guaranteed reliability of the network. The formulated optimization is exceptionally troublesome to solve within a polynomial time, due to its complicated combinatorial structure. Hence, a distributed multi-agent reinforcement learning (D-MARL) algorithm is proposed for eco-vehicular edges, where multiple agents cooperatively learn to receive the best reward. First, the algorithm segments the centralized action space into multiple smaller groups. Based on the model-free distributed Q learner, each edge agent takes its actions from the respective group. Also, in each learning state, a software-defined controller chooses the global best action from individual bests of the distributed agents. Numerical results validate that our learning solution achieves near-optimal performances within a small number of training episodes as compared with existing baselines.

This paper investigates highly mobile vehicular networks from users' perspectives in highway transportation. Particularly, a centralized software-defined architecture is introduced in which centralized resources can be assigned, programmed, and controlled using the anchor nodes (ANs) of the edge servers. Unlike the legacy networks, where a typical user is served from only one access point (AP), in the proposed system model, a vehicle user is served from multiple APs simultaneously. While this increases the reliability and the spectral efficiency of the assisted users, it also necessitates an accurate power allocation in all transmission time slots. As such, a joint user association and power allocation problem is formulated to achieve enhanced reliability and weighted user sum rate. However, the formulated problem is a complex combinatorial problem, remarkably hard to solve. Therefore, fine-grained machine learning algorithms are used to efficiently optimize joint user associations and power allocations of the APs in a highly mobile vehicular network. Furthermore, a distributed single-agent reinforcement learning algorithm, namely SARL-MARL, is proposed which obtains nearly identical genie-aided optimal solutions within a nominal number of training episodes than the baseline solution. Simulation results validate that our solution outperforms existing schemes and can attain genie-aided optimal performances.

Streaming codes encode a string of source packets and output a string of coded packets in real time, which eliminate the queueing delay of block coding and are thus especially suitable for delay-sensitive applications. This work studies random linear streaming codes (RLSCs) and i.i.d. packet erasure channels. While existing works focused on the asymptotic error-exponent analyses, this work characterizes the error rate in the finite memory length regime and the contributions include: (i) A new information-debt-based description of the error event; (ii) A matrix-based characterization of the error rate; (iii) A closed-form approximation of the error rate that is provably tight for large memory lengths; and (iv) A new Markov-chainbased analysis framework, which can be of independent research interest. Numerical results show that the approximation, i.e. (iii), closely matches the exact error rate even for small memory length (≈ 20). The results can be viewed as a sequential- coding counterpart of the finite length analysis of block coding [Polyanskiy et al. 10] under the specialized setting of RLSCs.

Background: Adolescents with brain stem dysfunction may undergo many invasive treatments, and parents are often faced with making the decision to withdraw treatment. However, in the face of their child’s death, the spiritual practices of parents dealing with end-of-life decision-making remain under investigated. Purpose: This study explores the spiritual practices in parents making end-of-life decisions for adolescents on life support with brain stem dysfunction. Method: A descriptive phenomenological study was conducted through in-depth interviews with three parents of two adolescents in Taiwan. Data were analysed using Colaizzi’s seven-step protocol. Results: Three main themes emerged: (1) faith during decision-making, (2) struggles during decision-making, (3) transformation during decision-making. The findings indicate that “transforming the nature of hope” is the essence of the experience. Conclusion: Family-centred care, gaining insight into parental spiritual practices, and developing culturally-appropriate care are recommended.

In wireless networks, the link connectivity may be intermittent due to shadowing, burstiness of data arrival, or uncoordinated resource allocation. In this paper, we model the intermittence of links as Bernoulli distributed random channel states, termed intermittence channel states , and study the impact of the corresponding channel state information at the transmitter (CSIT) in a two-user Gaussian broadcast channel (BC). Moreover, due to the heterogeneous timeliness of intermittence channel states, the CSIT considered in this paper is hybrid . More specifically, the CSIT of each link can be perfectly (causally or non-causally) available, delayed, or not available. When the links are connected, we adopt a general setting that the received signal-to-noise ratios can be different. Our contribution is the characterization of the capacity regions of intermittent Gaussian BC to within bounded gaps for all combinations of hybrid CSIT, except for scenario DN (delayed CSIT of receiver 1, no CSIT of receiver 2). For scenario DN, we propose an opportunistic physical layer network coding scheme that achieves a strictly larger degree-of-freedom (DoF) region than the no-CSIT DoF region. As a corollary, single-user CSIT is able to increase the sum DoF for intermittent Gaussian BC (also the capacity region for the erasure BC, as a by-product). This result is in sharp contrast to the recent negative result by Davoodi and Jafar, where it is shown that for fast-fading multiple-input single-output BC with continuous channel states, single-user CSIT does not help at all in terms of sum DoF.