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Due to enhancements in Internet of Things (IoT) technology, users can now control numerous IoT devices that are integrated for a system (e.g., smart building management system, smart factory, etc.) through mobile user equipment (UE). As the number of controllable IoT devices increase, the amount of data that needs to be processed has increased along with the energy consumption. Since many IoT devices and most UEs are battery operated, minimizing the energy consumption is very important. One solution is to have a Multi-access Edge Computing (MEC) system conduct the computation instead of the IoT devices and UEs to help save their energy. In this paper, an energy-optimized offloading approach that uses MEC computing support to process cooperative tasks is investigated. An optimization problem model is developed to minimize the energy consumption of IoT devices and UEs that have service delay limits. The numerical results demonstrate that the proposed IoT and UE Popularity-based Energy Optimization (IPEO) scheme provides a better performance compared to the conventional MEC offloading methods.

Channel-shortening effect (CSE) in oxide thin film transistors (TFTs) is a crucial issue that must be resolved for applications in ultra-high-resolution displays. One of the origins of the CSE is the diffusion of a shallow donor such as hydrogen from other layers into the channel. In this study, we investigated for the first time the CSE of self-aligned Al-doped In-Sn-Zn-O (Al-ITZO) TFTs with planar and trench structures. The TFTs with planar structures exhibited severe negative $\text{V}_{ {{ON}}}$ shifts after an annealing process, whereas the TFTs with trench structures were barely affected, thereby exhibiting excellent ON/ OFF characteristics. The vertical channel in the trench TFT had higher resistance than the horizontal channel because of a back-sidewall roughness and thin channel. The high resistance of vertical channels played a significant role in determining the ON/ OFF characteristics of Al-ITZO TFT, where $\text{V}_{ {{ON}}}$ remained constant until the diffused shallow donors made the resistive vertical channels become conductive. Based on these unique operation characteristics, the suppression of CSE in a trench TFT was demonstrated even under a high annealing temperature. Trench TFT exhibited higher mobility, higher drain currents, and higher stability than planar TFT, thus making it suitable for ultra-high-resolution displays.

Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based treatment algorithms remains elusive. Aortic disease management has been defined by anatomic scalars, even though aortic disease progression initiates complex shape changes which are difficult to quantify. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics, that captures dynamic shape evolution. The fluctuation in total curvature (δK) captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law like behavior with rapidly increasingδKforming the hallmark of aortic disease. Classification accuracy for predicting aortic disease state (normal, mildly diseased with successful surgery, and severely diseased with failed surgical outcomes) approaches92.8 ± 1.7%. The analysis ofδKcan be applied on any 3D geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes.

In coronary artery bypass grafting (CABG) for patients on hemodialysis, there has been concern about “coronary steal”. This study aims to evaluate the influence of using an in situ internal thoracic artery (ITA) ipsilateral to a preexisting arteriovenous fistula (AVF) in dialysis-dependent patients undergoing CABG. Between 2004 and 2018, dialysis-dependent patients with AVFs who underwent CABG were enrolled. According to the locational relationship of AVFs and in situ ITA grafts, the patients were divided into the ipsilateral group (n = 22) and the contralateral group (n = 21). Inverse probability weighting analysis was used to estimate and compare the late clinical outcomes. The late cardiac-related adverse events were not significantly different between the two groups: “major adverse cardiovascular and cerebrovascular events (MACCE)” (p = 0.090), “composite outcome of recurrent angina and coronary re-intervention” (p = 0.600). The in situ ITA graft of CABG on the ipsilateral side to AVF was not a significant risk factor for MACCE or the composite outcome of recurrent angina and coronary re-intervention. There was no statistically significant difference in the graft patency between the groups. Therefore, it might not be necessary to avoid using an in situ ITA on the ipsilateral side of an upper-arm AVF for optimal coronary artery bypass grafting in dialysis-dependent patients.

BACKGROUND Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) are self-reporting tools that can measure important information about patients, such as health priorities, experience, and perception of outcome. The use of traditional objective measures such as vital signs and lab values can be supplemented with these self-reported patient measures to provide a more complete picture of a patient’s health status. Machine learning, the use of computer algorithms that improve automatically through experience, is a powerful tool in health care that often does not use subjective information shared by patients. However, machine learning has largely been based on objective measures and has been developed without patient or public input. Algorithms often do not have access to critical information from patients and may be missing priorities and measures that matter to patients. Combining objective measures with patient-reported measures can improve the ability of machine learning algorithms to assess patients’ health status and improve the delivery of health care. OBJECTIVE The objective of this scoping review is to identify gaps and benefits in the way machine learning is integrated with patient-reported outcomes for the development of improved public and patient partnerships in research and health care. METHODS We reviewed the following 3 questions to learn from existing literature about the reported gaps and best methods for combining machine learning and patient-reported outcomes: (1) How are the public engaged as involved partners in the development of artificial intelligence in medicine? (2) What examples of good practice can we identify for the integration of PROMs into machine learning algorithms? (3) How has value-based health care influenced the development of artificial intelligence in health care? We searched Ovid MEDLINE(R), Embase, PsycINFO, Science Citation Index, Cochrane Library, and Database of Abstracts of Reviews of Effects in addition to PROSPERO and the ClinicalTrials website. The authors will use Covidence to screen titles and abstracts and to conduct the review. We will include systematic reviews and overviews published in any language and may explore additional study types. Quantitative, qualitative, and mixed methods studies are included in the reviews. RESULTS The search is completed, and Covidence software will be used to work collaboratively. We will report the review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and Critical Appraisal Skills Programme for systematic reviews. CONCLUSIONS Findings from our review will help us identify examples of good practice for how to involve the public in the development of machine learning systems as well as interventions and outcomes that have used PROMs and PREMs. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/36395

Ultra Reliable Low Latency Communication (URLLC) mode is a promising 5G technology for various real-time applications, such as, autonomous vehicles, augmented reality, and factory automation. Several papers on URLLC exist, however, most papers only focus on ways to achieve high reliability, and lack mathematical analysis of delay related to packet errors, retransmissions, and bandwidth. In this paper, a M/G/1 queuing model is applied to analyze the erroneous transmission recovery delay of URLLC multi-user services, and using this model the minimum required bandwidth is derived and applied into an adaptive control scheme. The proposed Pollaczek-Khinchine (P-K) formula based quadratic optimization (PFQO) scheme optimizes the bandwidth requirement by controlling the maximum retransmission parameter of the hybrid automatic repeated request (HARQ) mechanism in URLLC. Simulation results show the bandwidth saving effect of the proposed PFQO scheme based on various signal to interference plus noise ratios (SINRs) and packet length distributions.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based pathogen identification relies on the ribosomal protein spectra provided in the proprietary database. Although these mass spectra can discern various pathogens at species level, the spectra-based method still has limitations in identifying closely-related microbial species. In this study, to overcome the limits of the current MALDI-TOF MS identification method using ribosomal protein spectra, we applied MALDI-TOF MS of low-mass profiling to the identification of two genetically related Bacillus species, the food-borne pathogen Bacillus cereus, and the insect pathogen Bacillus thuringiensis. The mass spectra of small molecules from 17 type strains of two bacilli were compared to the morphological, biochemical, and genetic identification methods of pathogens. The specific mass peaks in the low-mass range (m/z 500- 3,000) successfully identified various closely-related strains belonging to these two reference species. The intensity profiles of the MALDI-TOF mass spectra clearly revealed the differences between the two genetically-related species at strain level. We suggest that small molecules with low molecular weight, 714.2 and 906.5 m/z can be potential mass biomarkers used for reliable identification of B. cereus and B. thuringiensis.

Oxide thin-film transistors (TFTs) should be manufactured with high mobility and stability based on a self-aligned top-gate structure to drive high-end displays. In this study, the effect of oxygen plasma time over one cycle of plasma-enhanced atomic layer deposition (PEALD) SiO2 on the properties of top-gate oxide TFTs was investigated systemically. The subsurface reaction of oxygen plasma causes a difference in oxygen vacancy (Vo). In addition, hydrogen incorporation also differs according to plasma time. Considering Vo and hydrogen are donors, tendency of electric properties could be explained. These surface reactions and atomic incorporation also induce differences in the positive bias temperature stress (PBTS) stability. Based on oxygen plasma time of 2.0 s, a positive shift in threshold voltage (Vth) due to interfacial degradation was observed when the plasma was longer, while an abnormal negative shift due to H+ drift was observed when it was shorter. When the oxygen plasma time is 2.0 s, the TFT was free from the deterioration of the interface and SiO2. Based on this condition, a self-aligned TFT with superior performance including a high mobility of 31.1 cm2/V s, positive Vth and high stability of 0.016 V shifting during the PBTS was fabricated successfully.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based pathogen identification relies on the ribosomal protein spectra provided in the proprietary database. Although these mass spectra can discern various pathogens at species level, the spectra-based method still has limitations in identifying closely-related microbial species. In this study, to overcome the limits of the current MALDI-TOF MS identification method using ribosomal protein spectra, we applied MALDI-TOF MS of low-mass profiling to the identification of two genetically related