Cost analysis of one-time intravenous antibiotic doses in the emergency department

BACKGROUND

Research shows that one-time doses of intravenous (IV) antibiotics do not improve resolution of infection. Providers, however, continue to use them - especially in the emergency department. Very few studies have aimed to quantify the cost of this practice.

OBJECTIVES

The primary objective was to evaluate the difference in average total cost of emergency department (ED) stay between patients who received a one-time dose of intravenous antibiotics in the ED before discharging on oral antibiotics and patients who were just discharged on oral antibiotics. Secondary objectives were to evaluate the differences in durations of stay between the two groups, as well as the differences in adverse drug effects and need for healthcare contact after discharge.

METHODS

Chart review was conducted to identify patients who received and did not receive a one-time dose of IV antibiotics in the ED between April 30, 2020, and April 30, 2022. A micro-costing approach was used to determine ED-associated costs per patient. Comparisons in primary and secondary outcomes were performed using statistical inferential tests.

RESULTS

A total of 102 patients were analyzed in each group. Patients who received a one-time dose of intravenous antibiotics in the emergency department before being discharged on oral antibiotics had an average length of stay of 4.55 hours, as opposed to patients who did not receive a one-time dose of intravenous antibiotics before being discharged on oral antibiotics who had an average length of stay of 2.82 hours (absolute difference: 1.73 hours, p < 0.001). One-time dosing of intravenous antibiotics in the emergency department incurred an additional cost of approximately $556 per patient, totaling to over $56,000 in our study cohort.

CONCLUSION

The use of one-time intravenous antibiotics in the emergency department did not confer any additional benefits to patients. Use of one-time doses resulted in significantly reduced throughput in the emergency department and significantly increased healthcare costs.

Performance Assessment for the Validation of Wireless Communication Engines in an Innovative Wearable Monitoring Platform

In today's health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors.

An Analysis of New York Data: Fluctuations in Hospital Capacity Are Driven by Variability in Elective Admissions and Discharge Activity

Background Hospital overcrowding compromises patient safety. The contribution of variability in admissions and discharges to overall hospital capacity needs to be quantified. This study describes the statewide day-to-day fluctuation in the volume of hospitalized patients, the variability and pattern of hospital admissions and discharges throughout the week, and the contribution of Emergency Department (ED) vs. elective (non-ED) admissions and discharges to the overall variability in the system across the week. Methodology This is a retrospective analysis of the New York State Statewide Planning and Research Cooperative System database, in which all New York healthcare facilities submit patient-level data monthly. The study period was from January 01 to December 31, 2015. Outcomes included total volumes of admissions and discharges and length of stay sorted by patient origin (ED vs. non-ED admits (elective)) and service type (medicine vs. surgery) by day of the week. Results We studied 1,692,090 hospital admissions. Admissions were highest on Mondays and Tuesdays and steadily decreased throughout the week. There was little variability in the ED admissions throughout the week. Surgical elective admissions had significant variability throughout the week, with higher admissions at the beginning of the week. There was a significant difference (p < 0.01) between admissions on weekdays vs. weekends. Discharges increased from Monday to Friday, with a dramatic drop on the weekends, for both ED and elective pathways. Systemwide, on Monday, hospitals were 21% above the mean volume, and on Fridays, hospitals were 32% below the mean volume. Conclusions Overall hospital capacity shows dramatic variability throughout the week, driven primarily by elective admissions and discharges from any source throughout the week. Because elective admissions are schedulable, hospitals can reduce variability by smoothing scheduling. Increased weekend discharges will also improve capacity.

Improving Postanesthesia Care Unit (PACU) Delays: A Quality Improvement Project

PURPOSE

Delays within the postanesthesia care unit (PACU) are a major cause of complications and inefficiency. In this project, we investigated the factors associated with delays in the PACU and implemented policies to mitigate these factors.

DESIGN

A quality improvement project.

METHODS

Data were collected for 10 months and included 1,134 surgical patients in a tertiary Obstetrics and Gynecology hospital in Kuwait. Several meetings were held with stakeholders to identify and overcome the reasons contributing to delays within the PACU.

FINDINGS

Among the top reasons for PACU delay were manpower shortage and lack of bed availability in the surgical wards due to improper admission and discharge policies. Policies were implemented to improve admission policy, hasten patient discharge, and improve patient flow through the operating theater (OT). These policies lead to a significant reduction (25 minutes) in the average time patients spend in the OT, mainly by reducing the stay in the PACU by 19 minutes.

CONCLUSIONS

PACU delays were mostly due to reasons outside the OT. Further, follow-up is needed to assess the sustainability of these improvements and identify any new challenges that may arise.

Principles of synaptic encoding of brainstem circadian rhythms

Circadian regulation of autonomic tone and reflex pathways pairs physiological processes with the daily light cycle. However, the underlying mechanisms mediating these changes on autonomic neurocircuitry are only beginning to be understood. The brainstem nucleus of the solitary tract (NTS) and adjacent nuclei, including the area postrema and dorsal motor nucleus of the vagus, are key candidates for rhythmic control of some aspects of the autonomic nervous system. Recent findings have contributed to a working model of circadian regulation in the brainstem which manifests from the transcriptional, to synaptic, to circuit levels of organization. Vagal afferent neurons and the NTS possess rhythmic clock gene expression, rhythmic action potential firing, and our recent findings demonstrate rhythmic spontaneous glutamate release. In addition, postsynaptic conductances also vary across the day producing subtle changes in membrane depolarization which govern synaptic efficacy. Together these coordinated pre- and postsynaptic changes provide nuanced control of synaptic transmission across the day to tune the sensitivity of primary afferent input and likely govern reflex output. Further, given the important role for the brainstem in integrating cues such as feeding, cardiovascular function and temperature, it may also be an underappreciated locus in mediating the effects of such non-photic entraining cues. This short review focuses on the neurophysiological principles that govern NTS synaptic transmission and how circadian rhythms impacted them across the day.

Single-Response Duplexing of Electrochemical Label-Free Biosensor from the Same Tag

Multiplexing is a valuable strategy to boost throughput and improve clinical accuracy. Exploiting the vertical, meshed design of reproducible and low-cost ultra-dense electrochemical chips, the unprecedented single-response multiplexing of typical label-free biosensors is reported. Using a cheap, handheld one-channel workstation and a single redox probe, that is, ferro/ferricyanide, the recognition events taking place on two spatially resolved locations of the same working electrode can be tracked along a single voltammetry scan by collecting the electrochemical signatures of the probe in relation to different quasi-reference electrodes, Au (0 V) and Ag/AgCl ink (+0.2 V). This spatial isolation prevents crosstalk between the redox tags and interferences over functionalization and binding steps, representing an advantage over the existing non-spatially resolved single-response multiplex strategies. As proof of concept, peptide-tethered immunosensors are demonstrated to provide the duplex detection of COVID-19 antibodies, thereby doubling the throughput while achieving 100% accuracy in serum samples. The approach is envisioned to enable broad applications in high-throughput and multi-analyte platforms, as it can be tailored to other biosensing devices and formats.

The Hyperledger fabric as a Blockchain framework preserves the security of electronic health records

The Hyperledger Fabric (HF) framework is widely studied for securing electronic health records (EHRs) in the healthcare sector. Despite the various cross-domain blockchain technology (BCT) applications, little is known about the role of the HF framework in healthcare. The purpose of the systematic literature review (SLR) is to review the existing literature on the HF framework and its applications in healthcare. This SLR includes literature published between January 2015 and March 2023 in the ACM digital library, IEEE Xplore, SCOPUS, Springer, PubMed, and Google Scholar databases. Following the inclusion and exclusion criteria, a total of 57 articles emerged as eligible for this SLR. The HF framework was found to be useful in securing health records coming from the Internet of Medical Things (IoMT) and many other devices. The main causes behind using the HF framework were identified as privacy and security, integrity, traceability, and availability of health records. Additionally, storage issues with transactional data over the blockchain are reduced by the use of the HF framework. This SLR also highlights potential future research trends to ensure the high-level security of health records.

A High-Throughput, High-Containment Human Primary Epithelial Airway Organ-on-Chip Platform for SARS-CoV-2 Therapeutic Screening

COVID-19 emerged as a worldwide pandemic in early 2020, and while the rapid development of safe and efficacious vaccines stands as an extraordinary achievement, the identification of effective therapeutics has been less successful. This process has been limited in part by a lack of human-relevant preclinical models compatible with therapeutic screening on the native virus, which requires a high-containment environment. Here, we report SARS-CoV-2 infection and robust viral replication in PREDICT96-ALI, a high-throughput, human primary cell-based organ-on-chip platform. We evaluate unique infection kinetic profiles across lung tissue from three human donors by immunofluorescence, RT-qPCR, and plaque assays over a 6-day infection period. Enabled by the 96 devices/plate throughput of PREDICT96-ALI, we also investigate the efficacy of Remdesivir and MPro61 in a proof-of-concept antiviral study. Both compounds exhibit an antiviral effect against SARS-CoV-2 in the platform. This demonstration of SARS-CoV-2 infection and antiviral dosing in a high-throughput organ-on-chip platform presents a critical capability for disease modeling and therapeutic screening applications in a human physiology-relevant in vitro system.

Correlations among common emergency medicine physician performance measures: Mixed messages or balancing forces?

The increasing complexity of ED physician performance measures has resulted in significant challenges, including duplicative and conflicting measures that fail to account for different ED settings. We performed a cross sectional analysis of correlations between measures to characterize their relationships and determine if differences exist between academic versus non-academic ED settings. Pearson correlations were calculated for 12 measures among 220 ED physicians at 11 EDs. Higher admission rate was strongly correlated with higher CT utilization rate (R = 0.7, p < 0.01) and longer room to discharge time (R = 0.7, p < 0.01). Higher patients per hour was strongly correlated with shorter room to doctor time (R = -0.7, p < 0.01). Stronger measure correlations were found in the academic setting compared to the non-academic setting. Strong correlations between ED measures imply opportunities to reduce competing performance demands on clinicians. Differences in correlations at academic versus non-academic settings suggest that it may be inappropriate to apply the same performance standards across settings.

Age of Synchronization Minimization Algorithms in Wireless Networks with Random Updates under Throughput Constraints

This study considers a wireless network where multiple nodes transmit status updates to a base station (BS) through a shared bandwidth-limited channel. Considering the random arrival of status updates, we measure the data freshness with the age of synchronization (AoS) metric; specifically, we use the time elapsed since the latest synchronization as a metric. The objective of this study is to minimize the weighted sum of the average AoS of the entire network while meeting the minimum throughput requirement of each node. We consider both the central scheduling scenario and the distributed scheduling scenario. In the central scheduling scenario, we propose the optimal stationary randomized policy when the transmission feedback is unavailable and the max-weight policy when it is available. In the distributed scenario, we propose a distributed policy. The complexity of the three scheduling policies is significantly low. Numerical simulations show that the policies can satisfy the throughput constraint in the central controlling scenario and the AoS performance of the max-weight policy is close to the lower bound.

Particle Counting Methods Based on Microfluidic Devices

Particle counting serves as a pivotal constituent in diverse analytical domains, encompassing a broad spectrum of entities, ranging from blood cells and bacteria to viruses, droplets, bubbles, wear debris, and magnetic beads. Recent epochs have witnessed remarkable progressions in microfluidic chip technology, culminating in the proliferation and maturation of microfluidic chip-based particle counting methodologies. This paper undertakes a taxonomical elucidation of microfluidic chip-based particle counters based on the physical parameters they detect. These particle counters are classified into three categories: optical-based counters, electrical-based particle counters, and other counters. Within each category, subcategories are established to consider structural differences. Each type of counter is described not only in terms of its working principle but also the methods employed to enhance sensitivity and throughput. Additionally, an analysis of future trends related to each counter type is provided.

Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning

Wireless resource utilizations are the focus of future communication, which are used constantly to alleviate the communication quality problem caused by the explosive interference with increasing users, especially the inter-cell interference in the multi-cell multi-user systems. To tackle this interference and improve the resource utilization rate, we proposed a joint-priority-based reinforcement learning (JPRL) approach to jointly optimize the bandwidth and transmit power allocation. This method aims to maximize the average throughput of the system while suppressing the co-channel interference and guaranteeing the quality of service (QoS) constraint. Specifically, we de-coupled the joint problem into two sub-problems, i.e., the bandwidth assignment and power allocation sub-problems. The multi-agent double deep Q network (MADDQN) was developed to solve the bandwidth allocation sub-problem for each user and the prioritized multi-agent deep deterministic policy gradient (P-MADDPG) algorithm by deploying a prioritized replay buffer that is designed to handle the transmit power allocation sub-problem. Numerical results show that the proposed JPRL method could accelerate model training and outperform the alternative methods in terms of throughput. For example, the average throughput was approximately 10.4-15.5% better than the homogeneous-learning-based benchmarks, and about 17.3% higher than the genetic algorithm.

Twenty Years of Advancements in a Radiotherapy Facility: Clinical Protocols, Technology, and Management

BACKGROUND

Hypo-fractionation can be an effective strategy to lower costs and save time, increasing patient access to advanced radiation therapy. To demonstrate this potential in practice within the context of temporal evolution, a twenty-year analysis of a representative radiation therapy facility from 2003 to 2022 was conducted. This analysis utilized comprehensive data to quantitatively evaluate the connections between advanced clinical protocols and technological improvements. The findings provide valuable insights to the management team, helping them ensure the delivery of high-quality treatments in a sustainable manner.

METHODS

Several parameters related to treatment technique, patient positioning, dose prescription, fractionation, equipment technology content, machine workload and throughput, therapy times and patients access counts were extracted from departmental database and analyzed on a yearly basis by means of linear regression.

RESULTS

Patients increased by 121 ± 6 new per year (NPY). Since 2010, the incidence of hypo-fractionation protocols grew thanks to increasing Linac technology. In seven years, both the average number of fractions and daily machine workload decreased by -0.84 ± 0.12 fractions/year and -1.61 ± 0.35 patients/year, respectively. The implementation of advanced dose delivery techniques, image guidance and high dose rate beams for high fraction doses, currently systematically used, has increased the complexity and reduced daily treatment throughput since 2010 from 40 to 32 patients per 8 h work shift (WS8). Thanks to hypo-fractionation, such an efficiency drop did not affect NPY, estimating 693 ± 28 NPY/WS8, regardless of the evaluation time. Each newly installed machine was shown to add 540 NPY, while absorbing 0.78 ± 0.04 WS8. The COVID-19 pandemic brought an overall reduction of 3.7% of patients and a reduction of 0.8 fractions/patient, to mitigate patient crowding in the department.

CONCLUSIONS

The evolution of therapy protocols towards hypo-fractionation was supported by the use of proper technology. The characteristics of this process were quantified considering time progression and organizational aspects. This strategy optimized resources while enabling broader access to advanced radiation therapy. To truly value the benefit of hypo-fractionation, a reimbursement policy should focus on the patient rather than individual treatment fractionation.

A simple and accurate LC‑MS/MS method for monitoring cyclosporin A that is suitable for high throughput analysis

With time, the number of samples in clinical laboratories from therapeutic drug monitoring has increased. Existing analytical methods for blood cyclosporin A (CSA) monitoring, such as high-performance liquid chromatography (HPLC) and immunoassays, have limitations including cross-reactivity, time consumption, and the complicated procedures involved. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has long been considered the reference standard owing to its high accuracy, specificity, and sensitivity. However, large numbers of blood samples, multi-step preparation procedures, and longer analytical times (2.5-20 min) are required as a consequence of the different technical strategies, to ensure good analytical performance and routine quality assurance. A stable, reliable, and high throughput detection method will save personnel time and reduce laboratory costs. Therefore, a high throughput and simple LC-MS/MS method was developed and validated for the detection of whole-blood CSA with CSA-d12 as the internal standard in the present study. Whole blood samples were prepared through a modified one-step protein precipitation method. A C18 column (50x2.1 mm, 2.7 µm) with a mobile phase flow rate of 0.5 ml/min was used for chromatographic separation with a total running time of 4.3 min to avoid the matrix effect. To protect the mass spectrometer, only part of the sample after LC separation was allowed to enter the mass spectrum, using two HPLC systems coupled to one mass spectrometry. In this way, throughput was improved with detection of two samples possible within 4.3 min using a shorter analytical time for each sample of 2.15 min. This modified LC-MS/MS method showed excellent analytical performance and demonstrated less matrix effect and a wide linear range. The design of multi-LC systems coupled with one mass spectrometry may play a notable role in the improvement of daily detection throughput, speeding up LC-MS/MS, and allowing it to be an integral part of continuous diagnostics in the near future.

Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture

A significant technological transformation has recently occurred in the agriculture sector. Precision agriculture is one among those transformations that largely focus on the acquisition of the sensor data, identifying the insights, and summarizing the information for better decision-making that would enhance the resource usage efficiency, crop yield, and substantial quality of the yield resulting in better profitability, and sustainability of agricultural output. For continuous crop monitoring, the farmlands are connected with various sensors that must be robust in data acquisition and processing. The legibility of such sensors is an exceptionally challenging task, which needs energy-efficient models for handling the lifetime of the sensors. In the current study, the energy-aware software-defined network for precisely selecting the cluster head for communication with the base station and the neighboring low-energy sensors. The cluster head is initially chosen according to energy consumption, data transmission consumption, proximity measures, and latency measures. In the subsequent rounds, the node indexes are updated to select the optimal cluster head. The cluster fitness is assessed in each round to retain the cluster in the subsequent rounds. The network model's performance is assessed against network lifetime, throughput, and network processing latency. The experimental findings presented here show that the model outperforms the alternatives presented in this study.

A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks

Disaster monitoring is a primary task for wireless sensor networks. Systems for the rapid reporting of earthquake information are a crucial aspect of disaster monitoring. Furthermore, during emergency rescue after a large earthquake, wireless sensor networks can provide pictures and sound information to save lives. Therefore, when accompanied by multimedia data flow, the alert and seismic data sent by the seismic monitoring nodes must be sufficiently fast. We present herein the architecture of a collaborative disaster-monitoring system that can obtain seismic data in a highly energy-efficient manner. In this paper, a hybrid superior node token ring MAC scheme is proposed for disaster monitoring in wireless sensor networks. This scheme consists of set-up and steady-state stages. A clustering approach was proposed for heterogeneous networks during the set-up stage. The proposed MAC operates in the duty cycle mode at the steady-state stage and is based on the virtual token ring of ordinary nodes, the polling all the superior nodes in one period, and alert transmissions with a low-power listening and shortened preamble approach during the sleep state. The proposed scheme can simultaneously satisfy the requirements of three types of data in disaster-monitoring applications. Based on embedded Markov chains, a model of the proposed MAC was developed and the mean queue length, mean cycle time, and mean upper bound of the frame delay were obtained. Using simulations under various conditions, the clustering approach performed better than the pLEACH approach, and the theoretical results of the proposed MAC were verified. We found that alerts and superior data have outstanding delay and throughput performances even under heavy traffic intensity, and the proposed MAC can provide a data rate of several hundred kb/s for superior and ordinary data. Considering all three types of data, the frame delay performances of the proposed MAC are better than those of the WirelessHART and DRX schemes, and the alert data of the proposed MAC have a maximum frame delay of 15 ms. These satisfy the application requirements of disaster monitoring.

An Adaptive TTT Handover (ATH) Mechanism for Dual Connectivity (5G mmWave-LTE Advanced) during Unpredictable Wireless Channel Behavior

Fifth Generation (5G) signals using the millimeter wave (mmWave) spectrums are highly vulnerable to blockage due to rapid variations in channel link quality. This can cause the devices or User Equipment (UE) to suffer from connection failure. In a dual connectivity (DC) network, the channel's intermittency issues were partially solved by maintaining the UE's connectivity to primary (LTE advanced stations) and secondary (5G mmWave stations) simultaneously. Even though the dual-connected network performs excellently in maintaining connectivity, its performance drops significantly due to the inefficient handover from one 5G mmWave station to another. The situation worsens when UE travels a long distance in a highly dense obstacle environment, which requires multiple ineffective handovers that eventually lead to performance degradation. This research aimed to propose an Adaptive TTT Handover (ATH) mechanism that deals with unpredictable 5G mmWave wireless channel behaviors that are highly intermittent. An adaptive algorithm was developed to automatically adjust the handover control parameters, such as Time-to-Trigger (TTT), based on the current state of channel condition measured by the Signal-to-Interference-Noise Ratio (SINR). The developed algorithm was tested under a 5G mmWave statistical channel model to represent a time-varying channel matrix that includes fading and the Doppler effect. The performance of the proposed handover mechanism was analyzed and evaluated in terms of handover probability, latency, and throughput by using the Network Simulator 3 tool. The comparative simulation result shows that the proposed adaptive handover mechanism performs excellently compared to conventional handovers and other enhancement techniques.

A sensitive, robust and high-throughput isotope dilution LC-MS/MS method for quantifying three folate forms in serum

This study aimed to establish an isotope dilution LC-MS/MS method for the determination of folic acid, 5-formyltetrahydrofolate and 5-methyltetrahydrofolate in human serum. This method was then used to quantify these three folate forms in the healthy adult population and supplement users. A stable 96-well solid-phase extraction system was used to prepare serum samples. The highly sensitive method was established using a Shimadzu LCMS-8060NX. The linearity was good in the range of 0.1-10 nmol/l for folic acid and 5-formyltetrahydrofolate and 1.0-100 nmol/l for 5-methyltetrahydrofolate. The accuracy and precision were good. The method was sensitive, robust and high-throughput and could be used for the routine clinical monitoring of these three folate forms in the Chinese population.

A Location-Aware Resource Optimization for Maximizing Throughput of Emergency Outdoor-Indoor UAV Communication with FSO/RF

In emergency communication scenarios, unmanned aerial vehicles (UAVs) can be used as an air relay to provide higher-quality communication for indoor users. When bandwidth resources are scarce, the use of free space optics (FSO) technology will greatly improve the resource utilization of the communication system. Therefore, we introduce FSO technology into the backhaul link of outdoor communication, and use free space optical/radio frequency (FSO/RF) technology to realize the access link of outdoor indoor communication. The deployment location of UAVs will affect not only the through wall loss of outdoor-indoor communication but also the quality of FSO communication, and, therefore, it needs to be optimized. In addition, by optimizing the power and bandwidth allocation of UAVs, we realize the efficient utilization of resources and improve the system throughput on the premise of considering information causality constraints and user fairness. The simulation results show that, by optimizing the location and power bandwidth allocation of UAVs, the system throughput is maximized, and the throughput between each user is fair.

Evaluation of the Aggregated Time Savings in Adopting Fast Brain MRI Techniques for Outpatient Brain MRI

INTRODUCTION

Clinical validation studies have demonstrated the ability of accelerated MRI sequences to decrease acquisition time and motion artifact while preserving image quality. The operational benefits, however, have been less explored. Here, we report our initial clinical experience in implementing fast MRI techniques for outpatient brain imaging during the COVID-19 pandemic.

METHODS

Aggregate acquisition times were extracted from the medical record on consecutive imaging examinations performed during matched pre-implementation (7/1/2019-12/31/2019) and post-implementation periods (7/1/2020-12/31/2020). Expected acquisition time reduction for each MRI protocol was calculated through manual collection of acquisition times for the conventional and accelerated sequences performed during the pre- and post-implementation periods. Aggregate and expected acquisition times were compared for the five most frequently performed brain MRI protocols: brain without contrast (BR-), brain with and without contrast (BR+), multiple sclerosis (MS), memory loss (MML), and epilepsy (EPL).

RESULTS

The expected time reductions for BR-, BR+, MS, MML, and EPL protocols were 6.6 min, 11.9 min, 14 min, 10.8 min, and 14.1 min, respectively. The overall median aggregate acquisition time was 31 [25, 36] min for the pre-implementation period and 18 [15, 22] min for the post-implementation period, with a difference of 13 min (42%). The median acquisition time was reduced by 4 min (25%) for BR-, 14.0 min (44%) for BR+, 14 min (38%) for MS, 11 min (52%) for MML, and 16 min (35%) for EPL.

CONCLUSION

The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols.

Development a high-throughput zebrafish embryo acute toxicity testing method based on OECD TG 236

The Organization for Economic Co-operation and Development (OECD）Test Guideline (TG) 236 for zebrafish embryo acute toxicity testing was adopted for chemical toxicity assessment in 2013. Due to the increasing demand for prediction and evaluation of the acute toxicity using zebrafish embryos, we developed a method based on OECD 236 test guideline with the aim to improve the testing efficiency. We used 4-128 cell stage zebrafish embryos and performed an exposure assay in a 96-well microtiter plate, observing the lethality endpoints of embryos at 48-h postexposure. A total of 32 chemicals (two batches) were used in the comparison study. Our results indicated that the logarithmic LC₅₀ (half lethal concentration) obtained by the modified method exhibited good correlation with that obtained by the OECD 236 testing method, and the R² of the linear regression analysis was 0.9717 (0.9621 and 0.9936 for the two batches, respectively). Additionally, the intra- and inter-laboratory coefficient of variation (CVs) for the LC₅₀ from the testing chemicals (17 chemicals in second batch) was less than 30%, except for CuSO₄. Therefore, the developed method was less time-consuming and demonstrated a higher throughput for toxicity testing compared to the prior method. We argue the developed method could be used as an additional choice for high-throughput zebrafish embryo acute toxicity test.

Implementation of Vertical Split Flow Model for Patient Throughput at a Community Hospital Emergency Department

BACKGROUND

Hospitals have implemented innovative strategies to address overcrowding by optimizing patient flow through the emergency department (ED). Vertical split flow refers to the concept of assigning patients to vertical chairs instead of horizontal beds based on patient acuity.

OBJECTIVE

Evaluate the impact of vertical split flow implementation on ED Emergency Severity Index (ESI) level 3, patient length of stay, and throughput at a community hospital.

METHODS

Retrospective cohort study of all ESI level 3 patients presenting to a community hospital ED over a 3-month period prior to and after vertical split flow implementation between 2018 and 2019.

RESULTS

In total, data were collected from 10,638 patient visits: 5262 and 5376 patient visits pre- and postintervention, respectively. There was a significant reduction in mean overall length of stay when ESI-3 patients were triaged with vertical split flow (251 min vs 283 min, p < 0.001).

CONCLUSIONS

Community hospital ED implementation of vertical split flow for ESI level 3 patients was associated with a significant reduction in overall length of stay and improved throughput. This model provides a solution to increase the number of patients that can be simultaneously cared for in the ED without increasing staffing or physical space.

Towards the Digital Twin (DT) of Narrow-Band Internet of Things (NBIoT) Wireless Communication in Industrial Indoor Environment

A study of the behavior of NB-IoT wireless communication in an industrial indoor environment was conducted in this paper. With Wireless Insite software, a scenario in the industrial sector was simulated and modeled. Our research examined how this scenario or environment affected the communication parameters of NB-IoT's physical layer. In this context, throughput levels among terminals as well as between terminals and transceiver towers, the power received at signal destination points, signal-to-noise ratios (SNRs) in the environment, and distances between terminals and transceivers are considered. These simulated results are also compared with the calculated or theoretical values of these parameters. The results show the effect of the industrial setting on wireless communication. The differences between the theoretical and simulated values are also established.

Resource Allocation for Reconfigurable Intelligent Surface Assisted Dual Connectivity

The next generation 6G wireless systems are envisioned to have higher reliability and capacity than the existing cellular systems. The reconfigurable intelligent surfaces (RISs)-assisted wireless networks are one of the promising solutions to control the wireless channel by altering the electromagnetic properties of the signal. The dual connectivity (DC) increases the per-user throughput by utilizing radio resources from two different base stations. In this work, we propose the RIS-assisted DC system to improve the per-user throughput of the users by utilizing resources from two base stations (BSs) in proximity via different RISs. Given an α-fair utility function, the joint resource allocation and the user scheduling of a RIS-assisted DC system is formulated as an optimization problem and the optimal user scheduling time fraction is derived. A heuristic is proposed to solve the formulated optimization problem with the derived optimal user scheduling time fractions. Exhaustive simulation results for coverage and throughput of the RIS-assisted DC system are presented with varying user, BS, blockage, and RIS densities for different fairness values. Further, we show that the proposed RIS-assisted DC system provides significant throughput gain of 52% and 48% in certain scenarios when compared to the existing benchmark and DC systems.

Experimental Performance Analysis of a Scalable Distributed Hyperledger Fabric for a Large-Scale IoT Testbed

Blockchain technology, with its decentralization characteristics, immutability, and traceability, is well-suited for facilitating secure storage, sharing, and management of data in decentralized Internet of Things (IoT) applications. Despite the increasing development of blockchain platforms, there is still no comprehensive approach for adopting blockchain technology in IoT systems. This is due to the blockchain’s limited capability to process substantial transaction requests from a massive number of IoT devices. Hyperledger Fabric (HLF) is a popular open-source permissioned blockchain platform hosted by the Linux Foundation. This article reports a comprehensive empirical study that measures HLF’s performance and identifies potential performance bottlenecks to better meet the requirements of blockchain-based IoT applications. The study considers the implementation of HLF on distributed large-scale IoT systems. First, a model for monitoring the performance of the HLF platform is presented. It addresses the overhead challenges while delivering more details on system performance and better scalability. Then, the proposed framework is implemented to evaluate the impact of varying network workloads on the performance of the blockchain platform in a large-scale distributed environment. In particular, the performance of the HLF is evaluated in terms of throughput, latency, network size, scalability, and the number of peers serviceable by the platform. The obtained experimental results indicate that the proposed framework can provide detailed real-time performance evaluation of blockchain systems for large-scale IoT applications.

Selfish Behavior in IEEE 802.11ah Networks: A Detection Algorithm and Mitigation Strategies

One of the latest protocols developed for the Internet of Things networks is IEEE 802.11ah, proposed by the WiFi Alliance. The new channel access mechanism in IEEE 802.11ah, which is called Restricted Access Window, aims at reducing the contention between the stations by allowing only selected stations to transmit data at certain time slots. Stations may exhibit selfish behavior to maximize their own throughput. This will come at the cost of the overall network quality of service. In this paper, we first analyze the default behavior of the IEEE 802.11ah protocol in terms of fairness. We then introduce various percentages of selfish stations and observe how the network's quality of service in terms of fairness, throughput and packet-loss are affected. After establishing the inherent fairness of IEEE 802.11ah, we analyze applicability of two existing selfish behavior detection algorithms designed for IEEE 802.11 to the IEEE 802.11ah protocol. Due to their poor performance, we propose a new definition of 'selfish behavior' specifically for IEEE 802.11ah, based on which we present a new algorithm for detecting selfish behavior. To combat selfish behavior and to create a better fairness, throughput and lower packet loss, we consequently present a novel mitigation algorithm called Selfish Stations Quarantine Punishment Algorithm (SSQPA). The proposed algorithm takes advantage of the RAW grouping to isolate selfish stations from the honest stations, thus mitigating the effect of the selfish behavior. SSQPA comes in two variants: honest stations-centric and network-centric. Our experimental results show that both variants can successfully mitigate selfish behavior effects in IEEE 802.11ah networks and either one can be used depending on the goal of the network.

Comprehensive Performance Analysis of Zigbee Communication: An Experimental Approach with XBee S2C Module

The recent development of wireless communications has prompted many diversified applications in both industrial and medical sectors. Zigbee is a short-range wireless communication standard that is based on IEEE 802.15.4 and is vastly used in both indoor and outdoor applications. Its performance depends on networking parameters, such as baud rates, transmission power, data encryption, hopping, deployment environment, and transmission distances. For optimized network deployment, an extensive performance analysis is necessary. This would facilitate a clear understanding of the trade-offs of the network performance metrics, such as the packet delivery ratio (PDR), power consumption, network life, link quality, latency, and throughput. This work presents an extensive performance analysis of both the encrypted and unencrypted Zigbee with the stated metrics in a real-world testbed, deployed in both indoor and outdoor scenarios. The major contributions of this work include (i) evaluating the most optimized transmission power level of Zigbee, considering packet delivery ratio and network lifetime; (ii) formulating an algorithm to find the network lifetime from the measured current consumption of packet transmission; and (iii) identifying and quantizing the trade-offs of the multi-hop communication and data encryption with latency, transmission range, and throughput.

Utilizing a Diffractive Focus Beam Shaper to Enhance Pattern Uniformity and Process Throughput during Direct Laser Interference Patterning

Uniform periodic microstructure formation over large areas is generally challenging in Direct Laser Interference Patterning (DLIP) due to the Gaussian laser beam intensity distribution inherent to most commercial laser sources. In this work, a diffractive fundamental beam-mode shaper (FBS) element is implemented in a four-beam DLIP optical setup to generate a square-shaped top-hat intensity distribution in the interference volume. The interference patterns produced by a standard configuration and the developed setup are measured and compared. In particular, the impact of both laser intensity distributions on process throughput as well as fill-factor is investigated by measuring the resulting microstructure height with height error over the structured surface. It is demonstrated that by utilizing top-hat-shaped interference patterns, it is possible to produce on average 44.8% deeper structures with up to 60% higher homogeneity at the same throughput. Moreover, the presented approach allows the production of microstructures with comparable height and homogeneity compared to the Gaussian intensity distribution with increased throughput of 53%.

A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

Performance evaluation of the fully automated random-access multiparameter Sysmex CN-6000 hemostasis analyzer at a core laboratory with a high sample throughput

INTRODUCTION

We aimed to evaluate the performance of the fully automated multiparameter CN-6000 hemostasis analyzer.

METHODS

Performance evaluation of the CN-6000 analyzer was conducted for 10 tests including prothrombin time (PT), activated partial prothrombin time (aPTT), fibrinogen level, anti-Xa activity, and antithrombin activity using a unique portfolio of liquid ready-to-use reagents. Precision, sample and reagent carryovers, throughput, and sample turnaround time (STAT) function were prospectively assessed. Results from 343 samples (normal subjects, critically ill patients, patients receiving anticoagulants, subjects with high or low fibrinogen levels, and patients with decreased levels of factor II, V, VII, and X) were compared to those obtained on the STA-R Max 2® analyzer using dedicated reagents.

RESULTS

Total precision (coefficient of variation) was below 7% for all parameters in both normal and pathological ranges. For all analyzed parameters, results obtained on the CN-6000 were strongly correlated with those obtained on the STA-R Max 2®analyzer. Agreement between both instruments was excellent for all assays. The CN-6000 demonstrated a 30% higher throughput compared to the STA-R Max 2® (258 vs 185 tests per hour for a panel of tests including PT, aPTT, fibrinogen, factor V, anti-Xa, and D-Dimer). STAT turnaround time for critical care samples testing was <7 minutes.

CONCLUSIONS

The CN-6000 analyzer performs equivalently or better than the STA-R Max 2® with a significantly improved throughput. This new hemostasis multiparameter analyzer appears to be particularly well suited for coagulation laboratories which require high sample throughput and manage high numbers of nonstandard and critical care samples.

A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-Enabled IoT

The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.

Implementation bottlenecks of the National Health Insurance program in Nepal: Paving the path towards Universal Health Coverage: A qualitative study

Most low and low-middle income countries adopting National Health Insurance (NHI) programs to achieve Universal Health Coverage are struggling to implement the program due to underlying problems at implementation. However, there is a lack of research that focuses on these problems. The Nepal NHI program initiated in 2016 has experienced numerous implementation challenges. This qualitative study delves into the NHI program's inputs and throughputs/implementation bottlenecks. The study based in Nepal's four districts included 28 in-depth interviews, six focus group discussions, and identified 12 themes that pointed to the NHI program's inadequate inputs causing bottlenecks. The analysis employed the Grounded Theory. The main challenges identified were insufficiently defined NHI implementations guidelines, conflicting Act clauses, a lack of HIB organizational guidelines, and inadequate human resources. The major throughput bottlenecks were difficulty enrolling the insurees, the inability to select the health providers competitively and to act as a prudent purchaser of the services. These inadequate inputs and throughput bottlenecks led to negative outputs such as insurees' high dropouts, and low coverage of poor households. The NHI program's sustainability might be at stake if the identified problems persist, further exacerbated by the plummeting economic situation in the country due to COVID-19.

Applicability of organ-on-chip systems in toxicology and pharmacology

Organ-on-chip (OoC) systems are microfabricated cell culture devices designed to model functional units of human organs by harboring an in vitro generated organ surrogate. In the present study, we reviewed issues and opportunities related to the application of OoC in the safety and efficacy assessment of chemicals and pharmaceuticals, as well as the steps needed to achieve this goal. The relative complexity of OoC over simple in vitro assays provides advantages and disadvantages in the context of compound testing. The broader biological domain of OoC potentially enhances their predictive value, whereas their complexity present issues with throughput, standardization and transferability. Using OoCs for regulatory purposes requires detailed and standardized protocols, providing reproducible results in an interlaboratory setting. The extent to which interlaboratory standardization of OoC is feasible and necessary for regulatory application is a matter of debate. The focus of applying OoCs in safety assessment is currently directed to characterization (the biology represented in the test) and qualification (the performance of the test). To this aim, OoCs are evaluated on a limited scale, especially in the pharmaceutical industry, with restricted sets of reference substances. Given the low throughput of OoC, it is questionable whether formal validation, in which many reference substances are extensively tested in different laboratories, is feasible for OoCs. Rather, initiatives such as open technology platforms, and collaboration between OoC developers and risk assessors may prove an expedient strategy to build confidence in OoCs for application in safety and efficacy assessment.

Development and assessment of a high-throughput biofilm and biomass testing platform

OBJECTIVE

To develop and evaluate a simple platform technology for developing static biofilms in a 96-well microtitre plate for various downstream applications. The technology allows monitoring of growth rate, biofilm formation and quantifying biofilm biomass by using crystal violet (CV) and safranin O (SO) staining over seven-day time periods for pathogens including clinical isolates most commonly associated with hard-to-treat wound infections.

METHOD

A total of 157 bacteria including Acinetobacter, Enterobacter, Klebsiella, Pseudomonas and Staphylococcus spp. were used in the study. Bacterial growth was measured at 600nm optical density (OD). Biofilm formation was monitored and assessed quantitatively with CV at 570nm and SO staining at 492nm for one-, two-, three- and seven-day incubation periods.

RESULTS

Bacterial growth rate and static biofilm biomass in the 96-well plates varied for various strains tested. Both CV and SO staining showed similar results in the biomass, with SO assay displaying more reproducible data throughout the study. Most of the strains were metabolically active even at the seven-day incubation period. Microbial adherences of all bacterial strains on the plastic surface was assessed with CV staining: 28 Acinetobacter, 17 Staphylococcus, 12 Pseudomonas and four Enterobacter strains were strong biofilm producers. Moderate biofilm-producing strains included 27 Staphylococcus, 14 Acinetobacter, eight Pseudomonas and three Enterobacter. Weak biofilm-producing strains included: 33 Staphylococcus, six Enterobacter, two Pseudomonas and one Acinetobacter. Only one Pseudomonas aeruginosa strain did not develop biofilm.

CONCLUSION

Our results demonstrate the feasibility of using 96-well microtitre plates as a high-throughput platform for quantitative measurement and assessment of biofilm development over time. Studying microbial adherence or biofilm biomass generated on various surfaces using a high-throughput system could provide valuable information for in vitro testing and developing therapeutics for biofilm infections. Employing the biofilm testing platform described in this study makes it possible to simultaneously develop different biofilms formed by specific pathogens, and study potential association between the quantity of bacterial biomass and strength of a biofilm formed by specific wound pathogens. In addition, the described testing approach could provide an optimal model for standardised and high-throughput screening of candidate antibiofilm therapeutics.

Seeing Red: Blood Sample Hemolysis Is Associated with Prolonged Emergency Department Throughput

INTRODUCTION

Hemolyzed emergency department (ED) blood specimens impose substantial burdens on various aspects of delivering care. The ED has the highest incidence of hemolysis among hospital departments. This study assessed the association and potential impact of hemolyzed blood samples on patient throughput time using ED length of stay (LOS) as the primary outcome measure.

METHODS

This study was a secondary analysis of data collected during a performance improvement project aimed at reducing the incidence of hemolysis in ED blood specimens. The electronic medical record was queried for potassium orders and results and for key patient throughput time points. Throughput times were stratified according to hemolysis, ED disposition (admitted vs discharged), and Emergency Services Index (ESI) triage categorization. Two-tailed t tests were used to compare throughput times for patients with and without hemolysis.

RESULTS

Potassium values were reported for 11 228 patient visits. The mean ED LOS was 287 minutes for patients with nonhemolyzed samples and 349 minutes for patients who had hemolyzed samples, a mean delay of 62 minutes. The mean throughput time for discharged patients was 92 minutes shorter in the group without hemolysis (337 vs 429 minutes). The mean throughput time for admitted patients was 28 minutes shorter in the group without hemolysis (264 vs 292 minutes). The increased LOS for patients with a hemolyzed blood sample was independent of the most commonly encountered ESI levels.

CONCLUSION

Hemolysis of blood samples obtained in the ED is associated with prolonged patient throughput via delays in patient disposition, independent of various markers of acuity, such as the patients' ultimate disposition or triage categorization.

Operationalizing the Use of Biofabricated Tissue Models as Preclinical Screening Platforms for Drug Discovery and Development

A wide range of complex in vitro models (CIVMs) are being developed for scientific research and preclinical drug efficacy and safety testing. The hope is that these CIVMs will mimic human physiology and pathology and predict clinical responses more accurately than the current cellular models. The integration of these CIVMs into the drug discovery and development pipeline requires rigorous scientific validation, including cellular, morphological, and functional characterization; benchmarking of clinical biomarkers; and operationalization as robust and reproducible screening platforms. It will be critical to establish the degree of physiological complexity that is needed in each CIVM to accurately reproduce native-like homeostasis and disease phenotypes, as well as clinical pharmacological responses. Choosing which CIVM to use at each stage of the drug discovery and development pipeline will be driven by a fit-for-purpose approach, based on the specific disease pathomechanism to model and screening throughput needed. Among the different CIVMs, biofabricated tissue equivalents are emerging as robust and versatile cellular assay platforms. Biofabrication technologies, including bioprinting approaches with hydrogels and biomaterials, have enabled the production of tissues with a range of physiological complexity and controlled spatial arrangements in multiwell plate platforms, which make them amenable for medium-throughput screening. However, operationalization of such 3D biofabricated models using existing automation screening platforms comes with a unique set of challenges. These challenges will be discussed in this perspective, including examples and thoughts coming from a laboratory dedicated to designing and developing assays for automated screening.

Dynamic Spectrum Sharing for Future LTE-NR Networks

5G is the next mobile generation, already being deployed in some countries. It is expected to revolutionize our society, having extremely high target requirements. The use of spectrum is, therefore, tremendously important, as it is a limited and expensive resource. A solution for the spectrum efficiency consists of the use of dynamic spectrum sharing, where an operator can share the spectrum between two different technologies. In this paper, we studied the concept of dynamic spectrum sharing between LTE and 5G New Radio. We presented a solution that allows operators to offer both LTE and New Radio services using the same frequency bands, although in an interleaved mode. We evaluated the performance, in terms of throughput, of a communication system using the dynamic spectrum sharing feature. The results obtained led to the conclusion that using the dynamic spectrum sharing comes with a compromise of a maximum 25% loss on throughput. Nevertheless, the decrease is not that substantial, as the mobile network operator does not need to buy an additional 15 MHz of bandwidth, using the already existing bandwidth of LTE to offer 5G services, leading to cost reduction and an increase in spectrum efficiency.

Algae to angiosperms: Autofluorescence for rapid visualization of plant anatomy among diverse taxa

PREMISE

Fluorescence microscopy is an effective tool for viewing plant internal anatomy. However, using fluorescent antibodies or labels hinders throughput. We present a minimal protocol that takes advantage of inherent autofluorescence and aldehyde-induced fluorescence in plant cellular and subcellular structures to markedly increase throughput in cellular and ultrastructural visualization.

METHODS AND RESULTS

Twelve species distributed across the plant phylogeny were each subjected to five fixative treatments: 1% paraformaldehyde and 2% glutaraldehyde, 2% paraformaldehyde, 2% glutaraldehyde, formalin-acid-alcohol (FAA), and 70% ethanol. Samples were prepared by embedding and mechanically sectioning or via whole mount. A confocal laser scanning system was used to collect micrographs. We evaluated and compared fixative influence on sample structural preservation and tissue autofluorescence.

CONCLUSIONS

Formaldehyde fixation of Viridiplantae taxa samples generates useful structural data while requiring no additional histological staining or clearing. In addition, a fluorescence-capable microscope is the only specialized equipment required for image acquisition. The minimal protocol developed in this experiment enables high-throughput sample processing by eliminating the need for multi-day preparations.

Evaluation of Child-Computer Interaction Using Fitts' Law: A Comparison between a Standard Computer Mouse and a Head Mouse

This study evaluates and compares the suitability for child-computer interaction (CCI, the branch within human-computer interaction focused on interactive computer systems for children) of two devices: a standard computer mouse and the ENLAZA interface, a head mouse that measures the user's head posture using an inertial sensor. A multidirectional pointing task was used to assess the motor performance and the users' ability to learn such a task. The evaluation was based on the interpretation of the metrics derived from Fitts' law. Ten children aged between 6 and 8 participated in this study. Participants performed a series of pre- and post-training tests for both input devices. After the experiments, data were analyzed and statistically compared. The results show that Fitts' law can be used to detect changes in the learning process and assess the level of psychomotor development (by comparing the performance of adults and children). In addition, meaningful differences between the fine motor control (hand) and the gross motor control (head) were found by comparing the results of the interaction using the two devices. These findings suggest that Fitts' law metrics offer a reliable and objective way of measuring the progress of physical training or therapy.

Collision Avoidance Resource Allocation for LoRaWAN

The number of connected IoT devices is significantly increasing and it is expected to reach more than two dozens of billions of IoT connections in the coming years. Low Power Wide Area Networks (LPWAN) have become very relevant for this new paradigm due to features such as large coverage and low power consumption. One of the most appealing technologies among these networks is LoRaWAN. Although it may be considered as one of the most mature LPWAN platforms, there are still open gaps such as its capacity limitations. For this reason, this work proposes a collision avoidance resource allocation algorithm named the Collision Avoidance Resource Allocation (CARA) algorithm with the objective of significantly increase system capacity. CARA leverages the multichannel structure and the orthogonality of spreading factors in LoRaWAN networks to avoid collisions among devices. Simulation results show that, assuming ideal radio link conditions, our proposal outperforms in 95.2% the capacity of a standard LoRaWAN network and increases the capacity by almost 40% assuming a realistic propagation model. In addition, it has been verified that CARA devices can coexist with LoRaWAN traditional devices, thus allowing the simultaneous transmissions of both types of devices. Moreover, a proof-of-concept has been implemented using commercial equipment in order to check the feasibility and the correct operation of our solution.

Point of Care Ultrasound First: An Opportunity to Improve Efficiency for Uncomplicated Pregnancy in the Emergency Department

Introduction: Research suggests emergency providers using point-of-care ultrasound (POCUS) to confirm an uncomplicated intrauterine pregnancy (IUP) can decrease emergency department (ED) length of stay (LOS) compared to a radiology department ultrasound (RADUS). The objective of this study was to compare the time to diagnosis and LOS between POCUS and RADUS patients. Methods: This was a retrospective study at one urban medical center. A standardized tool was used to abstract data from a random sample of pregnant patients diagnosed with uncomplicated IUP between January 2016 and December 2017 at a single tertiary care medical center. Microsoft Excel 2010 software was used to measure time intervals, prepare descriptive statistics, and perform Mann-Whitney U tests to compare differences. Results: A random sample of 836 (36%) of the 2,346 emergency department patients diagnosed with an IUP between 8-20 weeks' gestation during the study period was evaluated for inclusion. Three hundred sixty-six met inclusion criteria and were included in the final analysis. Patients were divided into 2 groups based on which type of ultrasound scan they received first: POCUS (n=165) and RADUS (n=201). Patients who received POCUS were found to have an IUP identified in an average of 48 minutes (95% CI, 43 to 53), while the RADUS group's mean time to diagnosis was 120 minutes (95% CI 113 to 127) with a difference of 72 minutes (95% CI, 63 to 80; p<0.001). The mean LOS for patients who received POCUS was 132 minutes (95% CI, 122 to 142), while that of the RADUS group was 177 minutes (95% CI 170 to 184) with a difference of 45 minutes (95% CI 32 to 56; p<0.001). The study is limited by its single-center, retrospective design and by lack of blinding of data abstractors. Conclusion: Pregnant emergency department patients diagnosed with an uncomplicated IUP between 8-weeks and 20-weeks' gestation had statistically significant reduction in time to diagnosis and disposition from the ED if assessed with POCUS as compared to RADUS.

Efficient Deployment with Throughput Maximization for UAVs Communication Networks

The article presents a throughput maximization approach for UAV assisted ground networks. Throughput maximization involves minimizing delay and packet loss through UAV trajectory optimization, reinforcing the congested nodes and transmission channels. The aggressive reinforcement policy is achieved by characterizing nodes, links, and overall topology through delay, loss, throughput, and distance. A position-aware graph neural network (GNN) is used for characterization, prediction, and dynamic UAV trajectory enhancement. To establish correctness, the proposed approach is validated against optimized link state routing (OLSR) driven UAV assisted ground networks. The proposed approach considerably outperforms the classical approach by demonstrating significant gains in throughput and packet delivery ratio with notable decrements in delay and packet loss. The performance analysis of the proposed approach against software-defined UAVs (U-S) and UAVs as base stations (U-B) verifies the consistency and gains in average throughput while minimizing delay and packet loss. The scalability test of the proposed approach is performed by varying data rates and the number of UAVs.

Improved Performance of SRAM-Based True Random Number Generator by Leveraging Irradiation Exposure

Encryption is an important step for secure data transmission, and a true random number generator (TRNG) is a key building block in many encryption algorithms. Static random-access memory (SRAM) chips can be easily available sources of true random numbers, benefiting from noisy SRAM cells whose start-up values flip between different power-on cycles. Embarking from this phenomenon, a novel performance (i.e., randomness and throughput) improvement method of SRAM-based TRNG is proposed, and its implementation can be divided into two phases: irradiation exposure and hardware postprocessing. As the randomness of original SRAM power-on values is fairly low, ionization irradiation is utilized to enhance its randomness, and the min-entropy can increase from about 0.03 to above 0.7 in the total ionizing irradiation (TID) experiments. Additionally, while the data remanence effect hampers obtaining random bitstreams with high speed, the ionization irradiation can also weaken this impact and improve the throughput of TRNG. In the hardware postprocessing stage, Secure Hash Algorithm 256 (SHA-256) is implemented on a Field Programmable Gate Array (FPGA) with clock frequency of 200 MHz. It can generate National Institute of Standards and Technology (NIST) SP 800-22 compatible true random bitstreams with throughput of 178 Mbps utilizing SRAM chip with 1 Mbit memory capacity. Furthermore, according to different application scenarios, the throughput can be widely scalable by adjusting clock frequency and SRAM memory capacity, which makes the novel TRNG design applicable for various Internet of Things (IOT) devices.

Midpoint Relay Selection Using Social Trust and Battery Level to Enhance Throughput in Cooperative Device-to-Device Communications

Device-to-device communications in underlay mode has emerged as a promising way to enhance spectrum efficiency in cellular networks. Recently, relay selection in D2D communications underlaying cellular networks is gaining more research interest. In this paper, we propose two relay selection schemes for D2D communications underlaying cellular networks, Midpoint Relay Selection using Social Trust and Battery Level (MRS-ST-BL) and Midpoint Relay Selection using Social Distance and Battery Level (MRS-SD-BL). These proposed schemes utilize battery power level information of devices together with social trust information of users in the network for relay selection. For performance evaluation, initially we show that the throughput of state-of-the-art schemes Hybrid Relay Selection (HRS) and our previously proposed schemes Midpoint Relay Selection using Social Trust (MRS-ST) and Midpoint Relay Selection Using Social Distance (MRS-SD) decrease, when relays have varying battery power. Then, we compare the performance of our proposed schemes against existing schemes including HRS, MRS-ST and MRS-SD. The performance comparison is done at various social trust scenarios and device densities. We show that our proposed schemes can significantly improve the throughput of D2D communications, particularly when relays have different battery power levels in weak social trust scenarios. Finally, we show that the performance of our proposed scheme MRS-ST-BL varies with the change in battery power threshold.

Medium Access Control Protocols for the Internet of Things Based on Unmanned Aerial Vehicles: A Comparative Survey

The Internet of Things (IoT), which consists of a large number of small low-cost devices, has become a leading solution for smart cities, smart agriculture, smart buildings, smart grids, e-healthcare, etc. Integrating unmanned aerial vehicles (UAVs) with IoT can result in an airborne UAV-based IoT (UIoT) system and facilitate various value-added services from sky to ground. In addition to wireless sensors, various kinds of IoT devices are connected in UIoT, making the network more heterogeneous. In a UIoT system, for achieving high throughput in an energy-efficient manner, it is crucial to design an efficient medium access control (MAC) protocol because the MAC layer is responsible for coordinating access among the IoT devices in the shared wireless medium. Thus, various MAC protocols with different objectives have been reported for UIoT. However, to the best of the authors' knowledge, no survey had been performed so far that dedicatedly covers MAC protocols for UIoT. Hence, in this study, state-of-the-art MAC protocols for UIoT are investigated. First, the communication architecture and important design considerations of MAC protocols for UIoT are examined. Subsequently, different MAC protocols for UIoT are classified, reviewed, and discussed with regard to the main ideas, innovative features, advantages, limitations, application domains, and potential future improvements. The reviewed MAC protocols are qualitatively compared with regard to various operational characteristics and system parameters. Additionally, important open research issues and challenges with recommended solutions are summarized and discussed.

Initial Clinical Experience Treating Patients With Lung Cancer on a 6MV-Flattening-Filter-Free O-Ring Linear Accelerator

Introduction Modern technologies, like intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT), have improved the therapeutic ratio of thoracic radiotherapy (TRT) for lung cancer (LC). Halcyon™ (Varian Medical Systems, Palo Alto, CA, USA), a novel 6MV-flattening-filter-free O-ring linear accelerator (6X-FFF ORL), was designed to deliver IMRT and VMAT with greater speed than a C-arm linac. Herein, we report our initial clinical experience treating patients with LC on this linac. Methods All patients who received TRT for LC on the 6X-FFF ORL at our institution were retrospectively identified. Patients' clinicopathologic data, radiotherapy details, early disease-control and toxicity outcomes, dosimetric data, couch corrections, and treatment times are reported. Results Between 10/2018-12/2019, 30 consecutive patients (median age 66 years, range 54-94 years) received definitive or post-operative TRT for LC (median 66 Gy/33 fractions; range 5-70 Gy/2-37 fractions) following four-dimensional computed tomography (CT) simulation (97%) using daily kilovoltage KV cone-beam CT (CBCT) (100%) on a 6X-FFF ORL for non-small cell LC (84%) or small cell LC (16%), with 53% receiving VMAT, 43% receiving static-field IMRT, and 77% receiving concurrent systemic therapy. All plans were approved through institutional peer review. The average three-dimensional vector couch correction based on CBCT guidance was 0.90 ± 0.50 cm. The average beam-on and beam on plus CBCT times were 1.7 ± 1.1 min, and 5.0 ± 3.2 min, respectively. Grade 3 dyspnea and fatigue occurred in 3% and 3% of patients, respectively. There were no grade ≥4 toxicities. Conclusion In this first clinical report of TRT for LC on a 6X-FFF ORL, daily CBCT-guided treatment was fast and safe with respect to dosimetry and clinical outcomes. Thus, use of this linac for TRT may increase LC patient throughput without a detriment in radiotherapy quality.

Late-afternoon communication and patient planning (CAPP) rounds: an intervention to allow early patient discharges

OBJECTIVE

Measure effect of late-afternoon communication and patient planning (CAPP) rounds to increase early electronic discharge orders (EDO).

METHODS

We enrolled 4485 patients discharged from six subspecialty medical services. We implemented late-afternoon CAPP rounds to identify patients who could have morning discharge the subsequent day. After an initial successful implementation of the intervention, we identified lack of sustainability. We made changes with sustained implementation of the intervention. This is a before-after study of a quality improvement intervention.

PROGRAM EVALUATION

Primary measures of intervention effectiveness were percentage of patients who received EDO by 11 am and patients discharged by noon. Additional measure of effectiveness were percent of patients admitted to the correct ward, emergency department (ED)-to-ward transfer time compared between intervention and nonintervention periods. We compared the overall expected LOS and the average weekly discharges to assess for comparability across the control and intervention time periods. We used the readmission rate as balancing measure to ensure that the intervention was not have unintended negative patients consequences.

RESULTS

Expected length of stay based upon discharge diagnosis/comorbidities and readmission rates were similar across the intervention and control time periods. The average weekly discharges were not statistically significant. Percentage of EDO by 11 am was higher in the first intervention period, second intervention period and combined intervention periods (28.9% vs. 21.8%, P < 0.001) compared with the respective control periods. Percent discharged before noon increased in the first intervention period, second intervention period and for the combined intervention periods (17 vs. 11.8%, P < 0.001). There was no difference in the percent admitted to the correct ward and ED-to-ward transfer time.

CONCLUSION

Afternoon CAPP rounds to identify early patient discharges the following day led to increase in EDO entered by 11 am and discharges by noon without an adverse change in readmission rates and LOS.

Impact of Provider-In-Triage in a Safety-Net Hospital

BACKGROUND

Increasing emergency department (ED) utilization has contributed to ED overcrowding, with longer ED length of stay (EDLOS) and more patients leaving without being seen (LWBS), and is associated with higher morbidity and mortality rates. Previous studies of provider in triage (PIT) have shown decreased LWBS, but variable improvements in EDLOS.

OBJECTIVES

We evaluated the impact of PIT implementation in an urban safety-net hospital on commonly reported ED throughput metrics.

METHODS

This before-and-after study was performed at an academic urban safety hospital. We implemented a PIT team that screened ambulatory ED patients for early discharge or expedited workup. The PIT intervention was implemented 3 days a week from January through April 2019. As controls, we compared throughput metrics from when PIT was unavailable (Group 2) and from 1 year prior (Group 3).

RESULTS

There were significantly (p < 0.001) lower rates of LWBS in Group 1 (4.8%, 95% confidence interval [CI] 4.1-5.8%) compared with 2 (7.3%, 95% CI 5.5-9.7%) and 3 (7.8%, 95% CI 6.9-9.0%). Door-to-doctor times were significantly (p < 0.001) lower for Group 1 (148 min, interquartile range [IQR] 88, 226 min) compared with 2 (187 min, IQR 95.5, 266 min) and 3 (215 min, IQR 131, 290 min). EDLOS was significantly (p < 0.001) shorter for Group 1 (337 min, IQR 215, 468 min) compared with 2 (385 min, IQR 271, 516 min) and 3 (413 min, IQR 299, 538 min).

CONCLUSIONS

We found significantly lower LWBS rates, shorter EDLOS, and shorter door-to-doctor times after PIT implementation. Compared with previous studies in a variety of settings, we found that PIT significantly improved LWBS and all throughput metrics in a safety net setting.

An Energy-Efficient and Adaptive Channel Coding Approach for Narrowband Internet of Things (NB-IoT) Systems

Most of the current research work on the Narrowband Internet of Things (NB-IoT) is focused on enhancing its network coverage. Many of the existing NB-IoT channel coding techniques are based on repeating transmission data and control signals as a way of enhancing the network’s reliability and therefore, enabling long-distance transmissions. Although most of these efforts are made at the expense of reducing the energy consumption of the NB-IoT network, they do not always consider the channel conditions. Therefore, this work proposes a novel NB-IoT Energy-Efficient Adaptive Channel Coding (EEACC) scheme. The EEACC approach is a two-dimensional (2D) approach which not only selects an appropriate channel coding scheme based on the estimated channel conditions (dynamically classified as bad, medium or good from initial based on a periodically assessed BLER performance outcome) but also minimizes the transmission repetition number under a pre-assessed probability of successful transmission (based on the ratio of previous successful transmissions over the total number of transmissions). This results in creating a single mixed gradient based on which a higher or lower Modulating Coding Scheme (MCS) is selected on each transmission. It is aimed at enhancing the overall energy efficiency of the network by dynamically selecting the appropriate Modulation Coding Scheme (MCS) number and efficiently minimizing the transmission repetition number. Link-level simulations are performed under different channel conditions (good, medium, or bad) considerations to assess the performance of the proposed up-link adaptation technique for NB-IoT. The obtained results demonstrate that the proposed technique outperforms the existing Narrowband Link Adaptation (NBLA) as well as the traditional repetition schemes in terms of the achieved energy efficiency as well as network reliability, latency, and scalability.