On the contribution of grain boundary sliding type creep to firn densification – an assessment using an optimization approach

Simulation approaches to firn densification often rely on the assumption that grain boundary sliding is the leading process driving the first stage of densification. Alley (1987) first developed a process-based material model of firn that describes this process. However, often so-called semi-empirical models are favored over the physical description of grain boundary sliding owing to their simplicity and the uncertainties regarding model parameters. In this study, we assessed the applicability of the grain boundary sliding model of Alley (1987) to firn using a numeric firn densification model and an optimization approach, for which we formulated variants of the constitutive relation of Alley (1987). An efficient model implementation based on an updated Lagrangian numerical scheme enabled us to perform a large number of simulations to test different model parameters and identify the simulation results that best reproduced 159 firn density profiles from Greenland and Antarctica. For most of the investigated locations, the simulated and measured firn density profiles were in good agreement. This result implies that the constitutive relation of Alley (1987) characterizes the first stage of firn densification well when suitable model parameters are used. An analysis of the parameters that result in the best agreement revealed a dependence on the mean surface mass balance. This finding may indicate that the load is insufficiently described, as the lateral components of the stress tensor are usually neglected in one-dimensional models of the firn column.

Simulation of COVID-19 Symptoms in a Genetically Engineered Mouse Model

The ongoing infectious viral disease pandemic (also known as the coronavirus disease-19; COVID-19) by a constantly emerging viral agent commonly referred as the severe acute respiratory syndrome corona virus 2 or SARS-CoV-2 has revealed unique pathological findings from infected human beings, and the postmortem observations. The list of disease symptoms, and post-mortem observations is too long to mention; however, a few notable ones are worth mentioning to put into a perspective in understanding the malignity of this pandemic starting with respiratory distress or dyspnea, chest congestion, muscle or body aches, malaise, fever, chills, etc. We opine that further improvement for delivering highly effective treatment, and preventive strategies would be benefited from validated animal disease models. In this context, we designed a study and show that a genetically engineered mouse expressing the human angiotensin converting enzyme 2; hACE2 (the receptor used by SARS-CoV-2 agent to enter host cells) represents an excellent investigative resource in simulating important clinical features of the COVID-19 infection. The hACE2 mouse model (which is susceptible to SARS-CoV-2) when administered with a recombinant SARS-CoV-2 spike (S) protein intranasally exhibited a profound cytokine storm capable of altering the physiological parameters including significant changes in in vivo cardiac function along with multi-organ damage that was further confirmed via histological findings. More importantly, visceral organs from SARS-CoV-2 spike (S) treated mice revealed thrombotic blood clots as seen during postmortem examination of the mice. Thus, the hACE2 engineered mouse appears to be a suitable model for studying intimate viral pathogenesis paving the way for further identification, and characterization of appropriate prophylactics as well as therapeutics for COVID-19 management.

A Simulation and Experimental Study on Identification of an Electromechanical System

The current applications in electromechanical energy conversion demand highly accurate speed and position control. For this purpose, a better understanding of the motion characteristics and dynamic behavior of electromechanical systems including nonlinear effects is needed. In this paper, a suitable model of Permanent Magnet Direct Current (PMDC) motor rotating in two directions is developed for identification purposes. Model is parameterized and identified via simulation and using real experimental data. Linear and nonlinear models for the system are built for identification, and the effective nonlinearities in the system, which are Coulomb friction and dead zone, are integrated into the nonlinear model. A Weiner- Hammerstein nonlinear system description is used for identification of the model. MATLAB is selected as the investigating tool, and a simulation model is used to observe the error between the simulated and estimated outputs. Identification of the linear and nonlinear system models using experimental data is performed using the least squares (LS) and recursive least squares (RLS) methods. Performance of the model and identification method with the real time experiments are presented numerically and graphically, revealing the advantages of the proposed nonlinear identification approach.

Process Parameters Optimization, Characterization, and Application of KOH-Activated Norway Spruce Bark Graphitic Biochars for Efficient Azo Dye Adsorption

In this work, Norway spruce bark was used as a precursor to prepare activated biochars (BCs) via chemical activation with potassium hydroxide (KOH) as a chemical activator. A Box–Behnken design (BBD) was conducted to evaluate and identify the optimal conditions to reach high specific surface area and high mass yield of BC samples. The studied BC preparation parameters and their levels were as follows: pyrolysis temperature (700, 800, and 900 °C), holding time (1, 2, and 3 h), and ratio of the biomass: chemical activator of 1: 1, 1.5, and 2. The planned BBD yielded BC with extremely high SSA values, up to 2209 m2·g−1. In addition, the BCs were physiochemically characterized, and the results indicated that the BCs exhibited disordered carbon structures and presented a high quantity of O-bearing functional groups on their surfaces, which might improve their adsorption performance towards organic pollutant removal. The BC with the highest SSA value was then employed as an adsorbent to remove Evans blue dye (EB) and colorful effluents. The kinetic study followed a general-order (GO) model, as the most suitable model to describe the experimental data, while the Redlich–Peterson model fitted the equilibrium data better. The EB adsorption capacity was 396.1 mg·g−1. The employment of the BC in the treatment of synthetic effluents, with several dyes and other organic and inorganic compounds, returned a high percentage of removal degree up to 87.7%. Desorption and cyclability tests showed that the biochar can be efficiently regenerated, maintaining an adsorption capacity of 75% after 4 adsorption–desorption cycles. The results of this work pointed out that Norway spruce bark indeed is a promising precursor for producing biochars with very promising properties.

Arabic Handwriting Classification using Deep Transfer Learning Techniques

Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable model for classifying the handwritten images written by the native or non-native. Two datasets comprised of Arabic handwriting images were used to evaluate and validate the newly developed deep learning models used to classify each model’s output as either native or foreign (non-native) writers. The training and validation sets were conducted using both original and augmented datasets. Results showed that the highest accuracy is using the GoogleNet deep learning model for both normal and augmented datasets, with the highest accuracy attained as 93.2% using normal data and 95.5% using augmented data in classifying the native handwriting.

Transposon-induced epigenetic silencing in the X chromosome as a novel form of dmrt1 expression regulation during sex determination in the fighting fish

Background Fishes are the one of the most diverse groups of animals with respect to their modes of sex determination, providing unique models for uncovering the evolutionary and molecular mechanisms underlying sex determination and reversal. Here, we have investigated how sex is determined in a species of both commercial and ecological importance, the Siamese fighting fish Betta splendens. Results We conducted association mapping on four commercial and two wild populations of B. splendens. In three of the four commercial populations, the master sex determining (MSD) locus was found to be located in a region of ~ 80 kb on LG2 which harbours five protein coding genes, including dmrt1, a gene involved in male sex determination in different animal taxa. In these fish, dmrt1 shows a male-biased gonadal expression from undifferentiated stages to adult organs and the knockout of this gene resulted in ovarian development in XY genotypes. Genome sequencing of XX and YY genotypes identified a transposon, drbx1, inserted into the fourth intron of the X-linked dmrt1 allele. Methylation assays revealed that epigenetic changes induced by drbx1 spread out to the promoter region of dmrt1. In addition, drbx1 being inserted between two closely linked cis-regulatory elements reduced their enhancer activities. Thus, epigenetic changes, induced by drbx1, contribute to the reduced expression of the X-linked dmrt1 allele, leading to female development. This represents a previously undescribed solution in animals relying on dmrt1 function for sex determination. Differentiation between the X and Y chromosomes is limited to a small region of ~ 200 kb surrounding the MSD gene. Recombination suppression spread slightly out of the SD locus. However, this mechanism was not found in the fourth commercial stock we studied, or in the two wild populations analysed, suggesting that it originated recently during domestication. Conclusions Taken together, our data provide novel insights into the role of epigenetic regulation of dmrt1 in sex determination and turnover of SD systems and suggest that fighting fish are a suitable model to study the initial stages of sex chromosome evolution.

Modelling of Liquid-Liquid Equilibria Using NRTL and UNIFAC Equations in the Extraction of Bio-Oil-Based Phenolics Produced from the Pyrolysis of Sugarcane Bagasse

An exploration on renewable energy resources has been paid more attention due to the depletion of the fossil-based energy resource. In addition, their safe and environmentally friendly properties have attracted experts’ interest. One of the renewable energy resources is the bio-oil produced from sugarcane bagasse. The bio-oil was produced through a pyrolysis at 500°C. However, the produced bio-oil showed a high content of phenolics, c.a. 40-60%. A liquid-liquid extraction to remove the phenolics using methanol-chloroform solvents would be beneficial to improve the stability of the bio-oil as well as to obtain high purity phenolics. Modelling of the liquid-liquid equilibria in the extraction was then developed using NRTL and UNIFAC equations. The empirical quantitative data of phase equilibrium system were calculated on both the extract and raffinate phases. The lowest RMSD value of 0.043160 was obtained from the calculations using NRTL equation at an extraction temperature of 50°C. Thus, the most suitable model was achieved using NRTL equation.

Canine transmissible venereal tumour established in immunodeficient mice reprograms the gene expression profiles associated with a favourable tumour microenvironment to enable cancer malignancy

Background Canine transmissible venereal tumours (CTVTs) can cross the major histocompatibility complex barrier to spread among dogs. In addition to the transmissibility within canids, CTVTs are also known as a suitable model for investigating the tumour–host immunity interaction because dogs live with humans and experience the same environmental risk factors for tumourigenesis. Moreover, outbred dogs are more appropriate than inbred mice models for simulating the diversity of human cancer development. This study built a new model of CTVTs, known as MCTVTs, to further probe the shaping effects of immune stress on tumour development. For xenotransplantation, CTVTs were first injected and developed in immunodeficient mice (NOD.CB17-Prkdcscid/NcrCrl), defined as XCTVTs. The XCTVTs harvested from NOD/SCID mice were then inoculated and grown in beagles and named mouse xenotransplantation of CTVTs (MCTVTs). Results After the inoculation of CTVTs and MCTVTs into immune-competent beagle dogs separately, MCTVTs grew faster and metastasized more frequently than CTVTs did. Gene expression profiles in CTVTs and MCTVTs were analysed by cDNA microarray to reveal that MCTVTs expressed many tumour-promoting genes involved in chronic inflammation, chemotaxis, extracellular space modification, NF-kappa B pathways, and focal adhesion. Furthermore, several well-known tumour-associated biomarkers which could predict tumour progression were overexpressed in MCTVTs. Conclusions This study demonstrated that defective host immunity can result in gene instability and enable transcriptome reprogramming within tumour cells. Fast tumour growth in beagle dogs and overexpression of tumour-associated biomarkers were found in a CTVT strain previously established in immunodeficient mice. In addition, dysregulated interaction of chronic inflammation, chemotaxis, and extracellular space modification were revealed to imply the possibly exacerbating mechanisms in the microenvironments of these tumours. In summary, this study offers a potential method to facilitate tumour progression and provide a niche for discovering tumour-associated biomarkers in cancer research.

Co-Creation Approach in Designing a Sustainable Coffee Supply Chain (a Case in Bandung Regency, West Java, Indonesia)

Agricultural development in Indonesia had been conducted in a top-down manner since its independence, which has limited its effectiveness due to the gap between the reality faced by the development actors and actors who are involved in the agricultural supply chain. This paper provides evidence of a more participative model design by involving most of the actors in its process. The study was performed in action research by using a co-creation approach, involving actors who contributed their thoughts in designing the most suitable model that can support them in reaching a more sustainable supply chain for coffee agribusiness in Bandung Regency, West Java, Indonesia. The results show that a co-creation approach has managed to improve the performance of the coffee supply chain by the formation of a cooperative, which enhanced coordination among stakeholders. Furthermore, the involvement of farmers provided significant contributions in the design of the model.

A Comparative Analysis of Machine Learning Algorithms in Design Process of Adaptive Traffic Signal Control System

In recent decades machine learning technology has proved its efficiency in most sectors by making human life easier. With this popularity and efficiency, it is applied to design traffic signal control systems to mitigate traffic congestion and distribute waiting delays. Hence, many researchers around the world are working to address this issue. As a part of the solution, this article presents a comparative analysis of various machine learning models to come up with a suitable model for an isolated intersection. In this context, eight machine learning models including Linear Regression, Ridge, Lasso, Support Vector Regression, k-Nearest Neighbour, Decision Tree, Random Forest, and Gradient Boosting Regression Tree are selected. Shivakumara Swamiji Circle (SSC), one of the intersections in Tumakuru, Karnataka, India is selected as a case study area. Essential data is collected from SSC through videography. The selected models are developed to predict green time based on traffic classification and volume in Passenger Car Units (PCU) for each phase on the PyCharm platform. The models are evaluated based on various performance metrics. Results revealed that all the selected models predict green splits with 91% accuracy using traffic classification as input, whereas, models showed 85% accuracy with PCU as input. And also, Gradient Boosting Regression Tree is the best suitable model for the selected intersection, whereas, Decision Tree is not referred model for this application.