Unifying AoI Minimization and Remote Estimation — Optimal Sensor/Controller Coordination with Random Two-way Delay

The ubiquitous usage of communication networks in modern sensing and control applications has kindled new interests on the timing coordination between sensors and controllers, i.e., how to use the waiting time\u27\u27 judicially to improve the system performance. Contrary to the common belief that a zero-wait policy is optimal, Sun et al. showed that a controller can strictly improve the data freshness, the so-called Age-of-Information (AoI), by postponing transmission in order to lengthen the duration of staying in a good state. The optimal waiting policy for the sensor side was later characterized in the context of remote estimation. Instead of focusing on the sensor and controller sides separately, this work develops the jointly optimal sensor/controller waiting policy in a Wiener-process system. This work generalizes the above two important results in the sense that not only do we consider joint sensor/controller designs (as opposed to sensor-only or controller-only schemes), but we also assume random delay in both the forward and feedback directions (as opposed to random delay in only one direction). In addition to provable optimality, extensive simulation is used to verify the performance of the proposed scheme

Distribution-oblivious Online Algorithms for Age-of-Information Penalty Minimization

The ever-increasing needs of supporting real-time applications have spurred new studies on minimizing Age-of-Information (AoI), a novel metric characterizing the data freshness of the system. This work studies the single-queue information update system and strengthens the seminal results of Sun et al. on the following fronts: (i) When designing the optimal offline schemes with full knowledge of the delay distributions, a new fixed-point-based method is proposed with quadratic convergence rate, an order-of-magnitude improvement over the state-of-the-art; (ii) When the distributional knowledge is unavailable (which is the norm in practice), two new low-complexity online algorithms are proposed, which provably attain the optimal average AoI penalty; and (iii) the online schemes also admit a modular architecture, which allows the designer to upgrade certain components to handle additional practical challenges. Two such upgrades are proposed for the situations: (iii.1) The AoI penalty function is also unknown and must be estimated on the fly, and (iii.2) the unknown delay distribution is Markovian instead of i.i.d. The performance of our schemes is either provably optimal or within 3% of the omniscient optimal offline solutions in all simulation scenarios

Review on the Conflicts between Offshore Wind Power and Fishery Rights: Marine Spatial Planning in Taiwan

In recent years, Taiwan has firmly committed itself to pursue the green energy transition and a nuclear-free homeland by 2025, with an increase in renewable energy from 5% in 2016 to 20% in 2025. Offshore wind power (OWP) has become a sustainable and scalable renewable energy source in Taiwan. Maritime Spatial Planning (MSP) is a fundamental tool to organize the use of the ocean space by different and often conflicting multi-users within ecologically sustainable boundaries in the marine environment. MSP is capable of definitively driving the use of offshore renewable energy. Lessons from Germany and the UK revealed that MSP was crucial to the development of OWP. This paper aims to evaluate how MSP is able to accommodate the exploitation of OWP in Taiwan and contribute to the achievement of marine policy by proposing a set of recommendations. It concludes that MSP is emerging as a solution to be considered by government institutions to optimize the multiple use of the ocean space, reduce conflicts and make use of the environmental and economic synergies generated by the joint deployment of OWP facilities and fishing or aquaculture activities for the conservation and protection of marine environments.Peer Reviewe

Connecting Gender, Race, Class, and Immigration Status to Disease Management

Objective: Chronic diseases are the leading causes of death in the United States. Chronic disease management occurs within all aspects of an individual’s life, including the workplace. Though the social constructs of gender, race, class, and immigration status within the workplace have been considered, their connection to disease management among workers has been less explicitly explored. Using a sample of immigrant hotel housekeepers, we explored the connections between these four social constructs and hypertension management. Methods: This qualitative research study was guided by critical ethnography methodology. Twenty-seven hotel room cleaners and four housemen were recruited (N = 31) and invited to discuss their experiences with hypertension and hypertension management within the context of their work environments. Results: Being a woman worker within the hotel industry was perceived to negatively influence participants’ experience with hypertension and hypertension management. In contrast, being a woman played a protective role outside the workplace. Being an immigrant played both a positive and a negative role in hypertension and its management. Being black and from a low socioeconomic class had only adverse influences on participants’ experience with hypertension and its management. Conclusion: Being a woman, black, lower class, and an immigrant simultaneously contribute to immigrant hotel housekeepers’ health and their ability to effectively manage their hypertension. The connection between these four constructs (gender, race, class, and immigration status) and disease management must be considered during care provision. Hotel employers and policy stakeholders need to consider those constructs and how they impact workers’ well-being. More studies are needed to identify what mitigates the associations between the intersectionality of these constructs and immigrant workers’ health and disease management within their work environment. Keywords: Gender, Race, Class, Immigration, Disease Management, Hospitalit

CCN2 Enhances Resistance to Cisplatin-Mediating Cell Apoptosis in Human Osteosarcoma

Osteosarcoma (OS) is the most common form of malignant bone tumor and is an aggressive malignant neoplasm exhibiting osteoblastic differentiation. Cisplatin is one of the most efficacious antitumor drugs for osteosarcoma patients. However, treatment failures are common due to the development of chemoresistance. CCN2 (also known as CTGF), is a secreted protein that binds to integrins, modulates the invasive behavior of certain human cancer cells. However, the effect of CCN2 in cisplatin-mediated chemotherapy is still unknown. Here, we found that CCN2 was upregulated in human osteosarcoma cells after treatment with cisplatin. Moreover, overexpression of CCN2 increased the resistance to cisplatin-mediated cell apoptosis. In contrast, reduction of CCN2 by CCN2 shRNA promoted the chemotherapeutic effect of cisplatin. We also found that CCN2 provided resistance to cisplatin-induced apoptosis through upregulation of Bcl-xL and survivin. Knockdown of Bcl-xL or survivin removed the CCN2-mediated resistance to apoptosis induced by cisplatin. On the other hand, CCN2 also promoted FAK, MEK, and ERK survival signaling pathways to enhance tumor survival during cisplatin treatment. In a mouse xenograft model, overexpression of CCN2 promoted resistance to cisplatin. However, knockdown of CCN2 increased the therapeutic effect of cisplatin. Therefore, our data suggest that CCN2 might be a critical oncogene of human osteosarcoma for cisplatin-resistance and supported osteosarcoma cell growth in vivo and in vitro

Implementation research and Asian American/Pacific Islander health

Numerous barriers prevent the translation of research into practice, especially in settings with diverse populations. Nurses are in contact with diverse populations across settings and can be an important influence to further implementation research. This paper describes conceptual approaches and methodological issues pertinent to implementation research and implications for Asian American/Pacific Islander (AAPI) health research. The authors discussed the values of using theory to guide implementation research, levels of theory that are commonly used in interventions, and decisions for theory selection. They also articulated the shortcoming of randomized controlled trials, the gold standard for testing efficacy of interventions, and present quasi-experimental designs as a plausible alternative to randomized controlled trials when research is conducted in real-world settings. They examined three types of quasi-experimental designs, the unit of analysis, the choice of dependent variables, and measurement issues that influence whether research findings and evidence-based interventions are successfully translated into practice. Practicing nurses who are familiar with the AAPI population, as well as nurse researchers who have expertise in AAPI health can play critical roles in shaping future implementation research to advance AAPI health. Nurses can provide practice-based evidence for refining evidence-supported interventions for diverse, real-world settings and theory-based interventions that are socioculturally appropriate for AAPIs. Interdisciplinary, practice-based research networks that bring multiple agencies, organizations, communities, and academic institutions together can be a mechanism for advancing implementation research for AAPI health

ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios