On the Use of LoRaWAN for Mobile Internet of Things: The Impact of Mobility

A long-range wide-area network (LoRaWAN) targets both mobile and static Internet of Things (IoT) applications; it is suited to IoT applications, which require a large coverage area while consuming less power at a low data rate; it provides a solution for transferring data between IoT devices with a minimum cost in terms of power, at the expense of higher latency. LoRaWAN was designed for static low-power long-range networks. However, several IoT solution applications involve the use of mobility. Therefore, this study investigates the usage of LoRaWAN in the field of mobile Internet of Things applications such as bike rentals, fleet monitoring, and wildlife and animal tracking applications. Using the OMNeT++ simulator, two different well-known mobility models are used to investigate the influence of mobility on the performance of mobile LoRaWAN. The results show that intense LoRaWAN networks can operate under a high velocity and varying traffic load. It can be observed that the random waypoint model combination yields a better performance, but at the cost of higher collisions and energy consumption. As a consequence, the results suggest the reconsideration of mobile IoT solutions over LoRaWAN.

Insights into standards of care: dexamethasone and antibodies against COVID-19 in hamster models

Rationale: In face of the ongoing SARS-CoV-2 pandemic, effective and well-understood treatment options are still scarce. While vaccines have proven instrumental in fighting SARS-CoV-2, their efficacy is challenged by vaccine hesitancy, novel variants and short-lasting immunity. Therefore, understanding and optimization of therapeutic options remains essential. Objectives: We aimed at generating a deeper understanding on how currently used drugs, specifically dexamethasone and anti-SARS-CoV-2 antibodies, affect SARS-CoV-2 infection and host responses. Possible synergistic effects of both substances are investigated to evaluate combinatorial treatments. Methods: By using two COVID-19 hamster models, pulmonary immune responses were analyzed to characterize effects of treatment with either dexamethasone, anti-SARS-CoV-2 spike monoclonal antibody or a combination of both. scRNA sequencing was employed to reveal transcriptional response to treatment on a single cell level. Measurements and main results: Dexamethasone treatment resulted in similar or increased viral loads compared to controls. Anti-SARS-CoV-2 antibody treatment alone or combined with dexamethasone successfully reduced pulmonary viral burden. Dexamethasone exhibited strong anti-inflammatory effects and prevented fulminant disease in a severe COVID-19-like disease model. Combination therapy showed additive benefits with both anti-viral and anti-inflammatory potency. Bulk and single-cell transcriptomic analyses confirmed dampened inflammatory cell recruitment into lungs upon dexamethasone treatment and identified a candidate subpopulation of neutrophils specifically responsive to dexamethasone. Conclusions: Our analyses i) confirm the anti-inflammatory properties and indicate possible modes of action for dexamethasone, ii) validate anti-viral effects of anti-SARS-CoV-2 antibody treatment, and iii) reveal synergistic effects of a combination therapy and can thus inform more effective COVID-19 therapies.

Multi-phase Rate Transient Behaviors of the Multi-fractured Horizontal Well with Complex Fracture Networks

Complex fracture networks (CFN) provide flow channels and significantly affect well performance in unconventional reservoirs. However, traditional rate transient analysis (RTA) models barely consider the effect of CFN on production performance. The impact of multi-phase flow on rate transient behaviors is still unclear especially under CFN. Neglecting these effects could cause incorrect rate transient response and erroneous estimation of well and fracture parameters. This paper investigates multi-phase rate transient behaviors considering CFN, and tries to investigate in what situations the multi-phase models should be used to obtain more accurate results. Firstly, an embedded discrete fracture model (EDFM) is generated instead of LGR method to overcome time-intensive computation. The model is coupled with reservoir models using non-neighboring connections (NNCs). Secondly, eight cases are designed using the EDFM technology to analyze effect of natural fractures, formation permeability, and relative permeability on rate transient behaviors. Thirdly, Blasingame plot, log-log plot, and linear flow plot are used to analyze the differences of rate transient response between single-phase and multi-phase flow in reservoirs with CFN. For multi-phase flow, severe deviations can be observed on RTA plots compared with single-phase model. Combination of three RTA type curves can characterize the differences from early to late flow regimes and improve the interpretation accuracy as well as reduce the non-unicity. Finally, field data analysis in Permian Basin demonstrates that multi-phase RTA analysis are required for analyzing production and pressure data since single-phase RTA analysis will lead to big errors especially under high water cut during fracturing fluid flowback period, early production of unconventional gas wells or after waterflooding or water huff-n-puff.

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficient of SPI and SPEI was 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing of time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.

Modeling cyanobacteria life cycle dynamics and historical nitrogen fixation in the Baltic Proper

Dense blooms of filamentous diazotrophic cyanobacteria are formed every summer in the Baltic Sea. These autotrophic organisms may bypass nitrogen limitation by performing nitrogen fixation, which also governs surrounding organisms by increasing bioavailable nitrogen. The magnitude of the nitrogen fixation is important to estimate from a management perspective since this might counteract eutrophication reduction measures. Here, a cyanobacteria life cycle model has been implemented for the first time in a high-resolution 3D coupled physical and biogeochemical model of the Baltic Sea, spanning the years 1850–2008. The explicit consideration of life cycle dynamics and transitions significantly improves the representation of the cyanobacterial phenological patterns compared to earlier 3D modeling efforts. Now, the rapid increase and decrease in cyanobacteria in the Baltic Sea are well captured, and the seasonal timing is in concert with observations. The current improvement also had a large effect on the nitrogen fixation load and is now in agreement with estimates based on in situ measurements. By performing four phosphorus sensitivity runs, we demonstrate the importance of both organic and inorganic phosphorus availability for historical cyanobacterial biomass estimates. The model combination can be used to continuously predict internal nitrogen loads via nitrogen fixation in Baltic Sea ecosystem management, which is of extra importance in a future ocean with changed conditions for the filamentous cyanobacteria.

Review and outlook of the integral test facility PKL III corresponding studies

This paper reviews an important integral test facility (ITF) named PKL (primary loop in German), which is designed based on a 4-loop pressurized water reactor (PWR) with the power 1 300 MWe, and especially concentrates on two aspects: (1) the tests at each developmental period of the facility until 2020, which is a typical microcosm of nuclear safety research; (2) the simulation of the PKL facility tests by using system thermal-hydraulic (STH) codes, especially RELAP5, TRACE and ATHLET. The results from the literature showed that all of these codes could reproduce the accident scenarios on the PKL facility to some extent, and simulate the complex phenomena both in the reactor pressurized vessel (RPV) and in the loops well, except some local phenomena (e. g., peak cladding temperature (PCT)). Furthermore, this paper presents some suggestions on PKL further tests. Especially, the sensitivity studies of initial conditions (ICs) and boundary conditions (BCs), test studies related to Extensive damage mitigation guidelines (EDMGs) and FLEX strategies, anticipated transients without scram (ATWS), detailed core section model, combination with other ITF or separate effects test (SET) facilities, and tests on advanced conception reactors are emphasized.

Transcription factor network analysis based on single cell RNA-seq identifies that Trichostatin-a reverses docetaxel resistance in prostate Cancer

Background Overcoming drug resistance is critical for increasing the survival rate of prostate cancer (PCa). Docetaxel is the first cytotoxic chemotherapeutical approved for treatment of PCa. However, 99% of PCa patients will develop resistance to docetaxel within 3 years. Understanding how resistance arises is important to increasing PCa survival. Methods In this study, we modeled docetaxel resistance using two PCa cell lines: DU145 and PC3. Using the Passing Attributes between Networks for Data Assimilation (PANDA) method to model transcription factor (TF) activity networks in both sensitive and resistant variants of the two cell lines. We identified edges and nodes shared by both PCa cell lines that composed a shared TF network that modeled changes which occur during acquisition of docetaxel resistance in PCa. We subjected the shared TF network to connectivity map analysis (CMAP) to identify potential drugs that could disrupt the resistant networks. We validated the candidate drug in combination with docetaxel to treat docetaxel-resistant PCa in both in vitro and in vivo models. Results In the final shared TF network, 10 TF nodes were identified as the main nodes for the development of docetaxel resistance. CMAP analysis of the shared TF network identified trichostatin A (TSA) as a candidate adjuvant to reverse docetaxel resistance. In cell lines, the addition of TSA to docetaxel enhanced cytotoxicity of docetaxel resistant PCa cells with an associated reduction of the IC50 of docetaxel on the resistant cells. In the PCa mouse model, combination of TSA and docetaxel reduced tumor growth and final weight greater than either drug alone or vehicle. Conclusions We identified a shared TF activity network that drives docetaxel resistance in PCa. We also demonstrated a novel combination therapy to overcome this resistance. This study highlights the usage of novel application of single cell RNA-sequencing and subsequent network analyses that can reveal novel insights which have the potential to improve clinical outcomes.

Towards a Singing Voice Multi-Sensor Analysis Tool: System Design, and Assessment Based on Vocal Breathiness

Singing voice is a human quality that requires the precise coordination of numerous kinetic functions and results in a perceptually variable auditory outcome. The use of multi-sensor systems can facilitate the study of correlations between the vocal mechanism kinetic functions and the voice output. This is directly relevant to vocal education, rehabilitation, and prevention of vocal health issues in educators; professionals; and students of singing, music, and acting. In this work, we present the initial design of a modular multi-sensor system for singing voice analysis, and describe its first assessment experiment on the ‘vocal breathiness’ qualitative characteristic. A system case study with two professional singers was conducted, utilizing signals from four sensors. Participants sung a protocol of vocal trials in various degrees of intended vocal breathiness. Their (i) vocal output, (ii) phonatory function, and (iii) respiratory behavior-per-condition were recorded through a condenser microphone (CM), an Electroglottograph (EGG), and thoracic and abdominal respiratory effort transducers (RET), respectively. Participants’ individual respiratory management strategies were studied through qualitative analysis of RET data. Microphone audio samples breathiness degree was rated perceptually, and correlation analysis was performed between sample ratings and parameters extracted from CM and EGG data. Smoothed Cepstral Peak Prominence (CPPS) and vocal folds’ Open Quotient (OQ), as computed with the Howard method (HOQ), demonstrated the higher correlation coefficients, when analyzed individually. DECOM method-computed OQ (DOQ) was also examined. Interestingly, the correlation coefficient of pitch difference between estimates from CM and EGG signals appeared to be (based on the Pearson correlation coefficient) statistically insignificant (a result that warrants investigation in larger populations). The study of multi-variate models revealed even higher correlation coefficients. Models studied were the Acoustic Breathiness Index (ABI) and the proposed multiple regression model CDH (CPPS, DOQ, and HOQ), which was attempted in order to combine analysis results from microphone and EGG signals. The model combination of ABI and the proposed CDH appeared to yield the highest correlation with perceptual breathiness ratings. Study results suggest potential for the use of a completed system version in vocal pedagogy and research, as the case study indicated system practicality, a number of pertinent correlations, and introduced topics with further research possibilities.

Calibration of basal melt on past ice discharge lowers projections of Antarctica’s sea level contribution

Antarctic mass loss is the largest contributor to uncertainties in sea level projections on centennial timescales. In this study the contribution of Antarctica’s ice discharge to future sea level changes is computed with ocean thermal forcing from 14 earth system models and linear response functions from 16 ice sheet models for three greenhouse gas emission scenarios. Different than in previous studies, basal melt was calibrated on observed Antarctic ice discharge rather than on basal melt itself with an iterative approach. For each model combination, a linear and quadratic melt dependency were calibrated both regionally (in five Antarctic sectors) and at the continental scale. Projections using all model combinations show that the variation in basal melt computation methods affect the projected sea level more than the scenario variations (SSP1-2.6 to SSP5-8.5). After calibration, a high number of model pairs still underestimated ice discharge in hindcasts over 1979–2017. Therefore top 10 % best-performing model combinations were selected for each method. A comparison between these model selections shows that the quadratic melt parameterisation with Antarctic-wide calibration performs best in reproducing past ice discharge. We conclude that calibration of basal melt on past ice discharge combined with model selection makes projections of Antarctic ice discharge (more) consistent with observations over the past four decades. Moreover, calibration of basal melt on past ice discharge results in lower basal melt sensitivities and thus lower projections of Antarctica’s sea level contribution than estimates of previous multi-model studies.