PV‐betriebene Umkehrosmoseanlage zur Meerwasserentsalzung – Modellierung und Analyse verschiedener Energieversorgungsvarianten

Reverse osmosis is a very energy‐efficient approach for seawater desalination. Since it requires only electrical energy, a combination of reverse osmosis and photovoltaic (PV) plants seems to be a promising solution. Therefore, a PV‐based reverse osmosis plant for seawater desalination for a location in Saudi Arabia is designed and modeled. On this basis, different options of a PV‐powered seawater desalination plant are investigated and conclusions for the construction of such plants are derived.     

On the correlation between T<Subscript>2</Subscript> and tissue diffusion coefficients in exercised muscle: quantitative measurements at 3T within the tibialis anterior

PURPOSE: To assess the transverse relaxation time T(2) and diffusion coefficient D before and following exercise in the tibialis anterior muscle and determine whether T(2) and D values were correlated. METHODS: Measurements of T(2) and D were performed at 3 T within axial slices through the calf muscles of six healthy volunteers at 95 s intervals before and for 10-12 min after a dorsiflexion exercise to exhaustion. RESULTS: The mean +/- standard deviation (SD) of T(2) and D before exercise were 32 +/- 1.55 ms and 1.52 +/- 0.15 mum(2)/ms, and after exercise were 43 +/- 2.5 ms and 1.72 +/- 0.13 mum(2)/ms, respectively. The mean +/- SD inter-individual recovery times of the % change in T(2) and D after exercise were 7.9 +/- 4.2 and 10.9 +/- 7.0 min, respectively. The T(2) and D values showed a significant correlation throughout the experiments (r (2) = 0.45). CONCLUSIONS: The increase in T(2) of skeletal muscle after exercise is correlated with the increase of the diffusion coefficient D and the recovery times appear similar, indicating that any model used to explain T(2) increases with exercise must also account for increased diffusion coefficients.     

Impact of environmental stress desiccation, acidity, alkalinity, heat or cold on antibiotic susceptibility of Cronobacter sakazakii

Cronobacter sakazakii is an emerging foodborne pathogen that has been implicated in severe forms of meningitis, septicemia or necrotizing colitis in pre-term neonates. Although illness outbreaks (primarily associated with powdered infant formula, PIF) caused by this pathogen are rare, the case-fatality rate may reach 50%. Successful treatment of C. sakazakii infection is reliant upon clinical use of antibiotics (AB) such as ampicillin. Recent reports showed increased resistance of C. sakazakii to broad-spectrum antibiotics. The objective of this study was to evaluate the effect of extreme pH (3.5 for 30 min or 11.25 for 5 min), cold (4 °C for 24 h), heat (55 °C for 5 min), and desiccation (cells were dried at 40 °C for 2 h and held at 21 °C for 4 d) stresses on susceptibility of five isolated strains of C. sakazakii to streptomycin, gentamicin, kanamycin, neomycin, tetracycline, doxycycline, tilmicosin, florfenicol, ampicillin, amoxicillin, vancomycin, ciprofloxacin and enrofloxacin. All unstressed strains of C. sakazakii were sensitive to streptomycin, gentamycin, kanamycin, ciprofloxacin, enrofloxacin, ampicillin and amoxicillin, but were moderately resistant or resistant to the rest. Exposing cells to alkaline or acidic stress did not change their sensitivity toward streptomycin, gentamycin, kanamycin or ciprofloxacin, but their resistance toward the other AB was increased. Cells stressed by desiccation showed increased sensitivity toward streptomycin, gentamicin, kanamycin, ciprofloxacin, enrofloxacin, ampicillin and doxycycline, but showed resistance toward the others. Cold-stressed cells were more sensitive to streptomycin, gentamicin, kanamycin, and ciprofloxacin compared with heat-stressed cells, but both heat and cold-stressed cells showed increased resistance toward all the other AB. Results obtained will help in understanding the effect of environmental stresses during processing on C. sakazakii susceptibility to AB.     

An Optimized CNN Model Architecture for Detecting Coronavirus (COVID-19) with X-Ray Images

This paper demonstrates empirical research on using convolutional neural networks (CNN) of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction. Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge. In this study, CNN architectures such as VGG-16, VGG-19, RestNet50, RestNet18 are compared, and an optimized model for feature extraction in X-ray images from various domains involving several classes is proposed. An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19 (Negative or Positive). Then, 2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models. Among those, the optimized model architecture classifier technique achieves higher accuracy (0.97) than four other models, specifically VGG-16, VGG-19, RestNet18, and RestNet50 (0.96, 0.72, 0.91, and 0.93, respectively). Therefore, this study will enable radiologists to more efficiently and effectively classify a patient’s coronavirus disease.     

Tuning of the activity and induction period of double metal cyanide catalyzed ring-opening polymerizations of propylene oxide by using ionic liquids

Polymerizations of propylene oxide (PO) have been carried out by using double metal cyanide (DMC) catalyst prepared by reacting ZnCl₂ and K₃[Co(CN)₆] in the presence of tert-butyl alcohol as a complexing agents. The DMC catalyst of the molecular formula, Zn_2.3Cl_1.0[Co(CN)₆]_1.0·2.0 ^tBuOH·1.0H₂O, is characterized by gas sorption measurements, infrared spectroscopy and X-ray powder diffraction. The structure of DMC catalyst with negligible surface area and broadened X-ray diffraction peaks is different from that of Prussian blue analogue, Zn₃[Co(CN)₆]₂·12H₂O of microporous crystalline materials. The PO polymerization behavior is tunable by combining it with various imidazolium based ionic liquids (ILs) as external additives. Thus, (1) they make the zinc-monomer bond faster activated during the initial stage of polymerization, (2) they make the zinc-monomer bond more active, (3) they stabilize the polymerization centers and prevent their decomposition, and (4) they improve important polymer properties such as molecular weight, viscosity and unsaturation level. The maximum rate of polymerization (R_p,max) of DMC catalyst increases from 2587 to 27,222 g-polymer/g-cat h by combining with 1-ethyl-3-methylimidazolium chloride (emimCl, [emimCl]/[Zn] = 1.25) at 115 °C. The induction period as the time to reach R_p,max becomes short from 321 min for DMC catalyst to 29 min for DMC/emimCl binary catalyst. The unsaturation value of polyol (0.017 mequiv./g) produced by DMC decreases to 0.005 mequiv./g by simply combining with IL. The molecular weight polyol produced by DMC catalyst increases from M_n = 3700 to more than 6000, and the viscosity of polyol decreases by combining with ILs.     

The variation of greenhouse gas emissions from soils of various land-use/cover types in Jambi province,Indonesia

We measured fluxes of three greenhouse gases (N₂O, CO₂O and CH₄) from soils of six different land-use types at 27 temporary field sites in Jambi Province, Sumatra, Indonesia. Study sites included natural and logged-over forests; rubber plantation; oil palm plantation; cinnamon plantation; and grassland field. The ranges of N₂O, CO₂ and CH₄ fluxes were 0.13–55.8 gN m-2h-1; 1.38–5.16 g C m-2d-1; –1.27–1.18 mg C m-2d-1, respectively. The averages of N₂O, CO₂ and CH₄ fluxes at 27 sites were 9.4 gN m-2h-1,3.65 g C m-2d-1, –0.45 mg C m-2d-1, respectively. The values of CO₂ and CH₄ fluxes were comparable with those in the reports regarding other humid tropical forests, while the N₂O flux was relatively lower than those of previous reports. The N₂O fluxes in each soil type were correlated with the nitrification rates of soils of 0–5 cm depth. In Andisols, the ratio of the N₂O emission rate to the nitrification rate was possibly smaller than that of the other soil types. There was no clear relationship between N₂O flux and the soil water condition, such as water-filled pore space. Seventeen percent of CH₄ fluxes were positive; according to these positive fluxes, we did not find a good correlation between CH₄ uptake rate and soil properties. Although we performed a chronosequence analysis to produce some hypotheses about the effect of land-use change by a limited amount of sampling at one point in time, further tests are required for the future.     

Safety of arteriovenous fistulae and grafts for continuous renal replacement therapy: The Michigan experience

Introduction: Arteriovenous fistula or graft (AVF/AVG) use is widely considered contraindicated for continuous renal replacement therapy (CRRT), yet insertion of hemodialysis (HD) catheters can carry high complication risk in critically ill end‐stage renal disease (ESRD) patients. Methods: Single‐center analysis of 48 consecutive hospitalized ESRD patients on maintenance HD who underwent CRRT using AVF/AVG from 2012 to 2013. Primary outcome was access‐related complications. Findings: Mean age was 60 years, 48% were male, and 88% required vasopressor support. Median duration of AVF/AVG use for CRRT was 4 days (range 1–34). Ten (21%) patients had access complications (5 bleeding, 5 infiltration, 1 thrombosis); 5 (10.4%) required catheter placement. Overall 31 (65%) patients survived to hospital discharge and AVF/AVG access was functional at the time of discharge in 29 (94%) patients. Discussion: In our experience, use of AVF/AVG for CRRT can be performed with a low serious complication rate and low risk of access loss, potentially avoiding catheter‐related complications.     

Using frame semantics for classifying and summarizing application store reviews

Text mining techniques have been recently employed to classify and summarize user reviews on mobile application stores. However, due to the inherently diverse and unstructured nature of user-generated online textual data, text-based review mining techniques often produce excessively complicated models that are prone to overfitting. In this paper, we propose a novel approach, based on frame semantics, for app review mining. Semantic frames help to generalize from raw text (individual words) to more abstract scenarios (contexts). This lower-dimensional representation of text is expected to enhance the predictive capabilities of review mining techniques and reduce the chances of overfitting. Specifically, our analysis in this paper is two-fold. First, we investigate the performance of semantic frames in classifying informative user reviews into various categories of actionable software maintenance requests. Second, we propose and evaluate the performance of multiple summarization algorithms in generating concise and representative summaries of informative reviews. Three different datasets of app store reviews, sampled from a broad range of application domains, are used to conduct our experimental analysis. The results show that semantic frames can enable an efficient and accurate review classification process. However, in review summarization tasks, our results show that text-based summarization generates more comprehensive summaries than frame-based summarization. Finally, we introduces MARC 2.0, a review classification and summarization suite that implements the algorithms investigated in our analysis.     

Hybrid Single Image Super-Resolution Algorithm for Medical Images

High-quality medical microscopic images used for diseases detection are expensive and difficult to store. Therefore, low-resolution images are favorable due to their low storage space and ease of sharing, where the images can be enlarged when needed using Super-Resolution (SR) techniques. However, it is important to maintain the shape and size of the medical images while enlarging them. One of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image resolution. Consequently, this paper suggests a multi-SR and classification framework based on Generative Adversarial Network (GAN) to generate high-resolution images with higher quality and finer details to reduce blurring. The proposed framework comprises five GAN models: Enhanced SR Generative Adversarial Networks (ESRGAN), Enhanced deep SR GAN (EDSRGAN), Sub-Pixel-GAN, SRGAN, and Efficient Wider Activation-B GAN (WDSR-b-GAN). To train the proposed models, we have employed images from the famous BreakHis dataset and enlarged them by 4× and 16× upscale factors with the ground truth of the size of 256 × 256 × 3. Moreover, several evaluation metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale Structural Similarity Index (MS-SSIM), and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency, training time, and storage space. The obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.     

Neighborhood Guided Smoothed Emphasis for Real Adaboost Ensembles

Neural Processing Letters - The exceptional capabilities of the much celebrated Real Adaboost ensembles for solving decision and classification problems are universally recognized. These...