Quantification of pulsed electric field for the rupture of giant vesicles with various surface charges, cholesterols and osmotic pressures

Electropermeabilization is a promising phenomenon that occurs when pulsed electric field with high frequency is applied to cells/vesicles. We quantify the required values of pulsed electric fields for the rupture of cell-sized giant unilamellar vesicles (GUVs) which are prepared under various surface charges, cholesterol contents and osmotic pressures. The probability of rupture and the average time of rupture are evaluated under these conditions. The electric field changes from 500 to 410 Vcm-1 by varying the anionic lipid mole fraction from 0 to 0.60 for getting the maximum probability of rupture (i.e., 1.0). In contrast, the same probability of rupture is obtained for changing the electric field from 410 to 630 Vcm-1 by varying the cholesterol mole fraction in the membranes from 0 to 0.40. These results suggest that the required electric field for the rupture decreases with the increase of surface charge density but increases with the increase of cholesterol. We also quantify the electric field for the rupture of GUVs containing anionic mole fraction of 0.40 under various osmotic pressures. In the absence of osmotic pressure, the electric field for the rupture is obtained 430 Vcm-1, whereas the field is 300 Vcm-1 in the presence of 17 mOsmL-1, indicating the instability of GUVs at higher osmotic pressures. These investigations open an avenue of possibilities for finding the electric field dependent rupture of cell-like vesicles along with the insight of biophysical and biochemical processes.

AMOC Trends From 1850 to 2100 At Interannual To Multi-Decadal Time Scales Corroborated By Changes In Salinity Budget

The Atlantic Meridional Overturning Circulation (AMOC) is a key player in the global coupled ocean-atmosphere climate system. To characterise the potential of an AMOC slowdown, a past and future trend probability analysis is applied using 16 models from the Coupled Model Intercomparison Project Phase 5. We determine the probability of AMOC annual to multidecadal trends under the historical period and two future climate scenarios (`business-as-usual’ scenario - RCP8.5 and `stabilisation’ scenario - RCP4.5). We show that the probability of a AMOC decline in model data shifts outside its range of intrinsic variability (determined from the pre-industrial control runs) for sustained 5-year trend or longer. This suggests that interannual AMOC events are not significantly affected by future climate scenario, and so potentially neither by anthropogenic forcing. Furthermore, under the ‘business-as-usual’ scenario the probability of a 20-year decline remains high (87\%) until 2100, however in a ‘stabilisation’ scenario the trend probability recovers its pre-industrial values by 2100. A 20-year unique event is identified from 1995 to 2015, marked by simultaneous unique features in the AMOC and salinity transport that are not replicated over any other 20-year period within the 250 years studied. These features include the maximum probability and magnitude of an `intense’ AMOC decline, and a sustained 20-year decline in subpolar salinity transport caused by internal oceanic processes (as opposed to external atmospheric forcing). This work therefore highlights the potential use of direct salinity transport observations, and ensemble mean numerical models to represent and understand changes in past, present, and future AMOC.

Data-driven bus timetabling with spatial-temporal travel time

PurposeTravel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.Design/methodology/approachThe authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.FindingsCase studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.Originality/valueThis study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

Contract design for the fourth party logistics considering tardiness risk

Nowadays, tardiness has become a significant risk in the logistics industry. To address this problem, we introduce the tardiness risk index to quantify both the magnitude of the tardiness risk and the maximum probability of tardiness occurring. In this paper, we investigate the contract design problem with the tardiness risk index to mitigate the tardiness risk when a fourth-party logistics company (4PL) delegates the delivery task of a client to a third-party logistics company (3PL). Specifically, the contracts are designed in a decentralized system with information symmetry and information asymmetry when 3PL is risk neutral and risk averse. Furthermore, the incentive problems demonstrated that the 3PL is encouraged to make the optimal effort for delivery and the 4PL determines the optimal fixed payment and penalty coefficient. Through analyzing the experimental simulation results, we can find that the contract can effectively mitigate the tardiness risk and the maximum probability of risk occurrence.

Analysis of health risks associated with food safety

Introduction. Aim. Selection of priority safety indicators and optimal research scope through analysis of potential health risks associated with chemical and microbiological safety of food products (FP). Materials and methods. Retrospectively analyzed data on chemical and microbiological safety of FP addressed on the consumer market of the Republic of Buryatia for 2016-2020. Assessment of the potential risk of harm to human health included prediction performed on linear regression models. Results. The maximum probability of violations of mandatory requirements for chemical and microbiological contamination was noted for dairy products. The minimum probability of violations was identified for the biologically active additives and industrial baby FP. The calculation of potential risks to consumer health based on the results of studies of FP revealed the categories of “high risk” - fish and seafood, “significant risk” - dairy products, confectionery, vegetables, melons, soft drinks. It was found that the supply of fish and seafood, poultry and poultry products had long supply chains, when the risks associated with non-compliance with their transportation and storage conditions were most likely Correlations were revealed between the risk level according to the microbiological criterion associated with the contamination of food, fish, culinary products, poultry meat and the incidence of acute intestinal infections. The indicated factor signs determine from 28.6% to 67.0% of the variance of the incidence. Conclusion. Identification of potential risks of harm to the public health related to FP safety indicates the need for further monitoring of the content of chemical and microbiological contaminants.

A preliminary study of commercially available general-purpose chest radiography artificial intelligence-based software for detecting airspace opacity lesions in COVID-19 patients

Purpose To validate commercially available general-purpose artificial intelligence (AI)-based software for detecting airspace opacity in chest radiographs (CXRs) of COVID-19 patients. Materials and Methods We used the ieee8023-covid-chestxray-dataset to validate commercial AI software capable of detecting "Nodule/Mass" and "Airspace opacity" as regions of interest with probability scores. From this dataset, we excluded computed tomography images and CXR images taken using an anteroposterior spine view and analyzed CXR images tagged with "Pneumonia/Viral/COVID-19" and "no findings". A radiologist then reviewed the images and rated them on a 3-point opacity score for the presence of airspace opacity. The maximum probability score of airspace opacity for each image was calculated using this software. The difference in each maximum probability for each opacity score was evaluated using Wilcoxon's rank sum test. The threshold of the probability score was determined by receiver operator characteristic curve analysis for the presence or absence of COVID-19, and the true positive rate (TPR) and false positive rate (FPR) were determined for the individual and overall opacity scores. Results Images from 342 patients with COVID-19 and 15 normal images were included. Opacity scores of 1, 2, and 3 were observed in 44, 70, and 243 images, respectively, of which 33 (75%), 66 (94.2%), and 243 (100%), respectively, were from COVID-19 patients. The overall TPR and FPR were 0.82 and 0.13, respectively, at an area under the curve of 0.88 and a threshold of 0.06, while the FPR for opacity score 1 was 0.18 and the TPR for score 3 was 0.97. Conclusion Using a public database containing CXR images of COVID-19 patients, commercial AI software was shown to be able to detect airspace opacity in severe pneumonia. Summary Commercially available AI software was capable of detecting airspace opacity in CXR images of COVID-19 patients in a public database.

Research on Identification of Power Grid Weakness Based on Bayesian Inference

With the gradual reform and development of the power grid, it is of great significance to study how to effectively identify and evaluate the weak links of the power grid for the actual planning, construction, and operation of the power grid. This paper analyzed the power grid’s historical component data and real-time operation state parameters. We established a weak link identification model based on Bayesian reasoning. Firstly, we constructed the node branch Bayesian network according to the network topology relationship. The power transmission distribution factor is modified according to the historical operation load of the grid components, and the conditional probability table is calculated based on the grid structure; finally, we used the maximum possible explanation algorithm in the Bayesian network. The weakness degree of all components in the network is calculated, and the maximum probability weak link sequence is obtained. The correctness and effectiveness of the proposed method are verified by IEEE 39 bus simulation and regional power grid data.

Influence of the structure of titenium phosphate on sorption properties

The process of sorption of lead, zinc, cadmium and cobalt ions from aqueous solutions on titanium-containing sorbents of various compositions is studied. Langmuir, Freundlich and Temkin models were used to determine sorption equilibrium. It was found that the process of sorption of metal ions on sorbents is described with the maximum probability by the Langmuir equation. The data obtained made it possible to determine the affinity of the metal to the sorbent and to compose a selectivity series.