A Recent Approach towards Fluidic Microstrip Devices and Gas Sensors: A Review

This paper aims to review some of the available tunable devices with emphasis on the techniques employed, fabrications, merits, and demerits of each technique. In the era of fluidic microstrip communication devices, versatility and stability have become key features of microfluidic devices. These fluidic devices allow advanced fabrication techniques such as 3D printing, spraying, or injecting the conductive fluid on the flexible/rigid substrate. Fluidic techniques are used either in the form of loading components, switching, or as the radiating/conducting path of a microwave component such as liquid metals. The major benefits and drawbacks of each technology are also emphasized. In this review, there is a brief discussion of the most widely used microfluidic materials, their novel fabrication/patterning methods.

A tutorial on reproducing a predefined autocovariance function through AR models: application to stationary homogeneous isotropic turbulence

Sequential methods for synthetic realisation of random processes have a number of advantages compared with spectral methods. In this article, the determination of optimal autoregressive (AR) models for reproducing a predefined target autocovariance function of a random process is addressed. To this end, a novel formulation of the problem is developed. This formulation is linear and generalises the well-known Yule-Walker (Y-W) equations and a recent approach based on restricted AR models (Krenk-Møller approach, K-M). Two main features characterise the introduced formulation: (i) flexibility in the choice for the autocovariance equations employed in the model determination, and (ii) flexibility in the definition of the AR model scheme. Both features were exploited by a genetic algorithm to obtain optimal AR models for the particular case of synthetic generation of homogeneous stationary isotropic turbulence time series. The obtained models improved those obtained with the Y-W and K-M approaches for the same model parsimony in terms of the global fitting of the target autocovariance function. Implications for the reproduced spectra are also discussed. The formulation for the multivariate case is also presented, highlighting the causes behind some computational bottlenecks.

From Quantum Codes to Gravity: A Journey of Gravitizing Quantum Mechanics

In this note, I review a recent approach to quantum gravity that “gravitizes” quantum mechanics by emerging geometry and gravity from complex quantum states. Drawing further insights from tensor network toy models in AdS/CFT, I propose that approximate quantum error correction codes, when re-adapted into the aforementioned framework, also have promise in emerging gravity in near-flat geometries.

Four In Silico Designed and Validated qPCR Assays to Detect and Discriminate Tilletia indica and T. walkeri, Individually or as a Complex

Several fungi classified in the genus Tilletia are well-known to infect grass species including wheat (Triticum). Tilletia indica is a highly unwanted wheat pathogen causing Karnal bunt, subject to quarantine regulations in many countries. Historically, suspected Karnal bunt infections were identified by morphology, a labour-intensive process to rule out other tuberculate-spored species that may be found as contaminants in grain shipments, and the closely-related pathogen T. walkeri on ryegrass (Lolium). Molecular biology advances have brought numerous detection tools to discriminate Tilletia congeners (PCR, qPCR, etc.). While those tests may help to identify T. indica more rapidly, they share weaknesses of targeting insufficiently variable markers or lacking sensitivity in a zero-tolerance context. A recent approach used comparative genomics to identify unique regions within target species, and qPCR assays were designed in silico. This study validated four qPCR tests based on single-copy genomic regions and with highly sensitive limits of detection (~200 fg), two to detect T. indica and T. walkeri separately, and two newly designed, targeting both species as a complex. The assays were challenged with reference DNA of the targets, their close relatives, other crop pathogens, the wheat host, and environmental specimens, ensuring a high level of specificity for accurate discrimination.

Gold(I) Complexes Bearing Alkylated 1,3,5-Triaza-7-phosphaadamantane Ligands as Thermoresponsive Anticancer Agents in Human Colon Cells

Overheating can affect solubility or lipophilicity, among other properties, of some anticancer drugs. These temperature-dependent changes can improve efficiency and selectivity of the drugs, since they may affect their bioavailability, diffusion through cell membrane or activity. One recent approach to create thermosensitive molecules is the incorporation of fluorine atoms in the chemical structure, since fluor can tune some chemical properties such as binding affinity. Herein we report the anticancer effect of gold derivatives with phosphanes derived from 1,3,5-triaza-7-phosphaadamantane (PTA) with long hydrocarbon chains and the homologous fluorinated chains. Besides, we analysed the influence of temperature in the cytotoxic effect. The studied gold(I) complexes with phosphanes derived from PTA showed antiproliferative effect on human colon carcinoma cells (Caco-2/TC7 cell line), probably by inhibiting cellular TrxR causing a dysfunction in the intracellular redox state. In addition, the cell cycle was altered by the activation of p53, and the complexes produce apoptosis through mitochondrial depolarization and the consequent activation of caspase-3. Furthermore, the results suggest that this cytotoxic effect is enhanced by hyperthermia and the presence of polyfluorinated chains.

Treatment of wastewater from separators for rainfall runoff using electrochemical oxidation processes

In recent years, industry has increased and with it the amount of oily wastewater, which are considered hazardous waste because they contain various types of heavy metals and oils that endanger the environment and human health. In the last twenty years, there has been increased research on new technologies to treat wastewater as efficiently and environmentally friendly as possible. A recent approach to wastewater treatment is the application of electrochemical processes such as the electro-Fenton process, which belongs to the group of electrochemical advanced oxidation processes and electrocoagulation. The aim of this work was to remove organic contaminants and heavy metals from wastewater originating from oil and grease separators that clean stormwater runoff from traffic areas. The use of stainless steel, iron and aluminum electrodes results in electrooxidation, electroreduction and electrocoagulation. At a current of 15 A the treatment efficiency was 50% for COD and 73% for mineral oil. At a current of 110 A the treatment efficiency was 96% for COD and 90% for mineral oil.

Student' own discoveries in information theory curriculum

A recent approach to learning Information and Coding Theory is suggested basing on power of modern computer science. Students willingly try to rediscover known and famous technologies by means of programming them. In order not to distract their attention, an ‘easy programming’ is suggested for what MATLAB seems to be the best tool. Collection of programs developed mutually by author and his students forms an ‘Information Theory Digital Laboratory’.

Improving Text-to-Code Generation with Features of Code Graph on GPT-2

Code generation, as a very hot application area of deep learning models for text, consists of two different fields: code-to-code and text-to-code. A recent approach, GraphCodeBERT uses code graph, which is called data flow, and showed good performance improvement. The base model architecture of it is bidirectional encoder representations from transformers (BERT), which uses the encoder part of a transformer. On the other hand, generative pre-trained transformer (GPT)—another multiple transformer architecture—uses the decoder part and shows great performance in the multilayer perceptron model. In this study, we investigate the improvement of code graphs with several variances on GPT-2 to refer to the abstract semantic tree used to collect the features of variables in the code. Here, we mainly focus on GPT-2 with additional features of code graphs that allow the model to learn the effect of the data stream. The experimental phase is divided into two parts: fine-tuning of the existing GPT-2 model, and pre-training from scratch using code data. When we pre-train a new model from scratch, the model produces an outperformed result compared with using the code graph with enough data.