Determining Factors of Organizational Learning Capabilities: In the context of educational institutes of third world country Pakistan

The purpose of this quantitative study was to test the dimensions of Organizational learning capability (OLC) and the reliability of the newly develop measurement instrument to capture those dimensions using structural equation modelling. Data was collected using Likert-style survey from teachers (N=150) in the context of public secondary schools (N=15) in the developing country, Pakistan. Methods included statistical equation modeling including gender as moderating variable. This study extends the literature on OLC to the demographic of educational institutes. The results of the analysis revealed the four-factor structure of managerial commitment (MC), systems perspective (SP), openness and experimentation (EX), and knowledge transfer and integration (TR) provides a reliable model of OLC, with a statistically significant correlation between the factors and OLC. The main effect in explaining changes in OLS was demonstrated by EX, with gender showing statistically significant in moderating the relationships of the variables with OLS.

A neuron, microglia, and astrocyte triple coculture model to study Alzheimer disease

Glial cells are essential to understand Alzheimer disease (AD) progression, given their role in neuroinflammation and neurodegeneration. There is a need for reliable and easy to manipulate models that allow studying the mechanisms behind neuron and glia communication. Currently available models such as co-cultures require complex methodologies and/or might not be affordable for all laboratories. With this in mind, we aimed to establish a straightforward in vitro setting with neurons and glial cells to study AD. We generated a triple co-culture with neurons, microglia and astrocytes. Immunofluorescence, western blot and ELISA techniques were used to characterize the effects of oligomeric Aβ (oAβ) in this model. We found that, in the triple co-culture, microglia increased the expression of anti-inflammatory markers Arginase I and TGF-β1, and reduced pro-inflammatory iNOS and IL-1β, compared with microglia alone. Astrocytes reduced expression of pro-inflammatory A1 markers AMIGO2 and C3, and displayed a ramified morphology resembling physiological conditions. Lastly, neurons increased post-synaptic markers, and developed more and longer branches than in individual primary cultures. Addition of oAβ in the triple co-culture reduced synaptic markers and increased microglial activation, which are hallmarks of AD. Consequently, we developed a reliable model, where cells better resemble physiological conditions: microglia are less inflammatory, astrocytes are less reactive and neurons display a more mature morphology than in individual primary cultures. Moreover, we were able to recapitulate Aβ-induced synaptic loss and inflammation. This model emerges as a powerful tool to study neurodegeneration and inflammation in the context of AD and other neurodegenerative diseases.

Monitoring the health status of water mains using a scorecard modelling approach

There has been considerable research into prediction of water mains failure, however, those models are very complex and fail to convey the message of the health status of an asset to the relevant stakeholders. The study developed a ‘pipe health scorecard’ based on historical failure data which could be used for operation, maintenance, refurbishment, or replacement decisions by a water utility. This scorecard model was developed by using 160,413 pipe-condition datasets from the South Australian Water Corporation over 10 years. Measures such as the Kolmogorov–Smirnov (KS) statistic, Area Under the ROC Curve (AUC), and Population Stability Index (PSI) showed the model is strong enough to predict the health status of water mains. The study found that the factors influencing water mains failure to be in the order of importance: length, material, age, location (road vs verge), diameter, and operating parameters. The development of a simple but reliable model for the assessment of the health status of water mains will have major benefits to the water utility with the ability to identify and potentially replace water pipes prior to failure. Additional benefits of flexible scheduling of the maintenance and replacement programs would contribute to cost savings.

Mathematical model for forecasting of violations in the field of veterinary medicine using the Markov chain

Activities within the field of veterinary medicine require an efficient governmental administration, including planning, programming, accounting and result analysis, in addition to control and supervision. As a result, it is crucial to have a theoretical understanding of the new forms of interaction between executive authorities at different levels and the economic entities carrying out these activities. Therefore, the improvement of the regulations governing this field is highly required. The solution is also linked to the improvement of the current legislation governing socio-economic processes, using a new reliable model. In this context, the main goal of this study is to develop proposals to improve the current legal regulation of administrative infractions in the field of veterinary medicine. The authors of this paper applied a multi-agent approach to identify groups of law subjects. They also developed a mathematical model using Markov chains to predict infractions in the field of veterinary medicine. The model is formed of “transition matrices” and takes into account the various reaction strategies of regulatory authorities. Such a strategy shows the likelihood of committing infractions and can form a basis for reforming the current legislation ruling the conduct of control and surveillance procedures using a risk-based approach.

Proteomic Changes of Activated Hepatic Stellate Cells

Hepatic stellate cells (HSC) are the major cellular drivers of liver fibrosis. Upon liver inflammation caused by a broad range of insults including non-alcoholic fatty liver, HSC transform from a quiescent into a proliferating, fibrotic phenotype. Although much is known about the pathophysiology of this process, exact cellular processes which occur in HSC and enable this transformation remain yet to be elucidated. In order to investigate this HSC transformation, we employed a simple, yet reliable model of HSC activation via an increase in growth medium serum concentration (serum activation). For that purpose, immortalized human LX-2 HSC were exposed to either 1% or 10% fetal bovine serum (FBS). Resulting quiescent (1% FBS) and activated (10% FBS) LX-2 cells were then subjected to in-depth mass spectrometry-based proteomics analysis as well as comprehensive phenotyping. Protein network analysis of activated LX-2 cells revealed an increase in the production of ribosomal proteins and proteins related to cell cycle control and migration, resulting in higher proliferation and faster migration phenotypes. Interestingly, we also observed a decrease in the expression of cholesterol and fatty acid biosynthesis proteins in accordance with a concomitant loss of cytosolic lipid droplets during activation. Overall, this work provides an update on HSC activation characteristics using contemporary proteomic and bioinformatic analyses and presents an accessible model for HSC activation. Data are available via ProteomeXchange with identifier PXD029121.

Galleria mellonella as a Good Model to Study Acinetobacter baumannii Pathogenesis

The invertebrate model, Galleria mellonella, has been widely used to study host–pathogen interactions due to its cheapness, ease of handling, and similar mammalian innate immune system. G. mellonella larvae have been proven to be useful and a reliable model for analyzing pathogenesis mechanisms of multidrug resistant Acinetobacter baumannii, an opportunistic pathogen difficult to kill. This review describes the detailed experimental design of G. mellonella/A. baumannii models, and provides a comprehensive comparison of various virulence factors and therapy strategies using the G. mellonella host. These investigations highlight the importance of this host–pathogen model for in vivo pathogen virulence studies. On the long term, further development of the G. mellonella/A. baumannii model will offer promising insights for clinical treatments of A. baumannii infection.

Liquid Density Prediction of Ethanol/Water, Using Artificial Neural Network

In this work, our objective was to get a reliable model for predicting liquid density ethanol-water and use it again later in modeling the ethanol production process from biomass. Hence, the unreliability of the Peng-Robinson equation of state to predict this property was shown. The average absolute deviation of this prediction is equal to 14.72 %. To have a reliable model, an artificial neural network (ANN) method was followed. Levenberg–Marquardt algorithm is used to choose the optimized ANN structure that has ten neurons in the hidden layer, three neurons in the input layer, and one neuron in the output layer, with a tangent-sigmoid and linear transfer functions, in the hidden and the output layers, respectively. The model training was done using 348 experimental data points from published experiments, realized at different liquid mole fraction range, pressure (0.10 to 10.00MP), and temperature (298.15 K to 476.2 K). The correlation coefficient between the experimental and liquid phase density was 0.9999 for training, validation, and testing the model. Statistical analysis is employed to evaluate the accuracy of the ANN, showing that the average absolute deviation, root mean square, and the Bias are 0.047 %, 0.003 %, and -0.004 %, respectively. So the ANN model gives a good estimation of liquid density, for mixture ethanol/water, with a relative importance of pressure, composition, and temperature equal to 41%, 34 %, and 25 %, respectively.

Multi-documentary cadastral audit of real estate objects

The current practice of cadastral and forensic activities shows its subjectivity. The expert makes a resolution in each specific case and for each specific real estate object. The traditional priority of a paper document over an electronic one also plays an important role. The article deals with the problem of the EGRN data reliability, which, according to the author, consists in the lack of unified approaches to gathering and processing source documents, analyzing the set of real estate objects characteristics, as well as their verification (audit). The purpose of the study is to develop a method of multi-documentary cadastral audit of real estate objects, which consists of a set of procedures for processing and analyzing documented cadastral information presented in the form of a disparate documents set. The results of the study showed the possibility of unifying and justifying a reliable model of a real estate object at any time, which is essential for verifying the reliability of the registered characteristics in the EGRN and formalizing expert activities. The developed method of multi-documentary cadastral audit simulates the work of a specialist and enables automating expert activities in the field of real estate, which meets modern trends in automation and digitalization.

Implementing a Proxy-Basin Strategy to Assess the Transposability of a Hydrological Model in Geographically Similar Catchments

Hydrological modelling is the most common way to investigate the spatial and temporal distribution of regional water resources. The reliability and uncertainty of a model depend on the efficient calibration of hydrological parameters. However, in complex regions where several subcatchments are defined, calibration of parameters is often difficult due to a lack of observed data. The transposability of hydrological models is of critical importance for assessing hydrological effects of land use and climatic changes in ungauged watersheds. Our study implemented a Proxy-Catchment Differential Split-Sample (PBDSS) strategy to assess the transposability of the conceptual hydrological model J2000 in three different subcatchments with similar physiographic conditions in Western Turkey. For dry and wet scenarios, the model was calibrated and validated for five years (2013–2017) in two selected catchments (Kayirli and Ulubey). Afterwards, it was validated by predicting the streamflow in the Amasya catchment, which has similar physical and climatic characteristics. The approach comprises transferring J2000 model parameters between different catchments, adjusting parameters to reflect the prevailing catchment characteristics, and validating without calibration. The objective functions showed a reliable model performance with Nash–Sutcliffe Efficiency (E) ranging from 0.72 to 0.82 when predicting streamflow in the study subcatchments for wet and dry conditions. An uncertainty analysis showed good agreement between the ensemble mean and measured runoff, indicating that the sensitive parameters can be used to estimate discharge in ungauged catchments. Therefore, the J2000 model can be considered adequate in its transposability to physically similar subcatchments for simulating daily streamflow.