Demand-Driven Multi-Target Sample Preparation on Resource-Constrained Digital Microfluidic Biochips

Microfluidic lab-on-chips offer promising technology for the automation of various biochemical laboratory protocols on a minuscule chip. Sample preparation (SP) is an essential part of any biochemical experiments, which aims to produce dilution of a sample or a mixture of multiple reagents in a certain ratio. One major objective in this area is to prepare dilutions of a given fluid with different concentration factors, each with certain volume, which is referred to as the demand-driven multiple-target (DDMT) generation problem. SP with microfluidic biochips requires proper sequencing of mix-split steps on fluid volumes and needs storage units to save intermediate fluids while producing the desired target ratio. The performance of SP depends on the underlying mixing algorithm and the availability of on-chip storage, and the latter is often limited by the constraints imposed during physical design. Since DDMT involves several target ratios, solving it under storage constraints becomes even harder. Furthermore, reduction of mix-split steps is desirable from the viewpoint of accuracy of SP, as every such step is a potential source of volumetric split error. In this article, we propose a storage-aware DDMT algorithm that reduces the number of mix-split operations on a digital microfluidic lab-on-chip. We also present the layout of the biochip with -storage cells and their allocation technique for . Simulation results reveal the superiority of the proposed method compared to the state-of-the-art multi-target SP algorithms.

Storage assignment optimization for fishbone robotic mobile fulfillment systems

Robotic Mobile Fulfillment System (RMFS) affects the traditional scheduling problems heavily while operating a warehouse. This paper focuses on storage assignment optimization for Fishbone Robotic Mobile Fulfilment Systems (FRMFS). Based on analyzing operation characteristics of FRMFS, a storage assignment optimization model is proposed with the objectives of maximizing operation efficiency and balancing aisle workload. Adaptive Genetic Algorithm (AGA) is designed to solve the proposed model. To validate the effectiveness of AGA in terms of iteration and optimization rate, this paper designs a variety of scenarios with different task sizes and storage cells. AGA outperforms other four algorithm in terms of fitness value and convergence and has better convergence rate and stability. The experimental results also show the advancement of AGA in large size FRMFS. In conclusion, this paper proposes a storage assignment model for FRMFS to reduce goods movement and travel distance and improve the order picking efficiency.

Verification of Hypsibius exemplaris Gąsiorek et al., 2018 (Eutardigrada; Hypsibiidae) application in anhydrobiosis research

Anhydrobiosis is considered to be an adaptation of important applicative implications because it enables resistance to the lack of water. The phenomenon is still not well understood at molecular level. Thus, a good model invertebrate species for the research is required. The best known anhydrobiotic invertebrates are tardigrades (Tardigrada), considered to be toughest animals in the world. Hypsibius. exemplaris is one of the best studied tardigrade species, with its name “exemplaris” referring to the widespread use of the species as a laboratory model for various types of research. However, available data suggest that anhydrobiotic capability of the species may be overestimated. Therefore, we determined anhydrobiosis survival by Hys. exemplaris specimens using three different anhydrobiosis protocols. We also checked ultrastructure of storage cells within formed dormant structures (tuns) that has not been studied yet for Hys. exemplaris. These cells are known to support energetic requirements of anhydrobiosis. The obtained results indicate that Hys. exemplaris appears not to be a good model species for anhydrobiosis research.

Metal ion toxicity and tolerance mechanisms in plants growing in acidic soil

<span>The abiotic factors have a wide effect on the growth of plants along with the cultivation of staple crops. The concentration of both essential and non-essential elements is impacted by number of biogeochemical factors. The low pH (≤5.0) of the soil is one such factor which poses variation in the levels of metal ions and mostly it leads to metal toxicity. The excess concentrations of the elements in the soil affects the growth, yield and the metabolic activities of the plants making them susceptible. However, some of the genotypes adapt themselves to metal toxicity condition by regulating their homeostatic genes leading to develop different strategies to undergo detoxification method. In the present review we discuss about the toxicity of Al, Fe and As which is a non-essential metal, an essential metal and an unwanted heavy metal. In a broad picture, to escape the toxic effects, plants have the strategy to exclude the excess metal outside the plant or include it in its storage cells. The insight of the present review aims at understanding these strategies in details which can be put into agricultural applications for developing better crops.</span>

Statistical Modelling of SPADs for Time-of-Flight LiDAR

Time-of-Flight (TOF) based Light Detection and Ranging (LiDAR) is a widespread technique for distance measurements in both single-spot depth ranging and 3D mapping. Single Photon Avalanche Diode (SPAD) detectors provide single-photon sensitivity and allow in-pixel integration of a Time-to-Digital Converter (TDC) to measure the TOF of single-photons. From the repetitive acquisition of photons returning from multiple laser shots, it is possible to accumulate a TOF histogram, so as to identify the laser pulse return from unwelcome ambient light and compute the desired distance information. In order to properly predict the TOF histogram distribution and design each component of the LiDAR system, from SPAD to TDC and histogram processing, we present a detailed statistical modelling of the acquisition chain and we show the perfect matching with Monte Carlo simulations in very different operating conditions and very high background levels. We take into consideration SPAD non-idealities such as hold-off time, afterpulsing, and crosstalk, and we show the heavy pile-up distortion in case of high background. Moreover, we also model non-idealities of timing electronics chain, namely, TDC dead-time, limited number of storage cells for TOF data, and TDC sharing. Eventually, we show how the exploit the modelling to reversely extract the original LiDAR return signal from the distorted measured TOF data in different operating conditions.

Epitaxial Growth of Optoelectronically Active Ga(As)Sb Quantum Dots on Al-Rich AlGaAs with GaAs Capsule Layers

We present a study of optoelectronically active Ga(As)As quantum dots (QDs) on Al-rich AlxGa1-xAs layers with Al concentrations up to x = 90%. So far, however, it has not been possible to grow optoelectronically active Ga(As)As QDs epitaxially directly on and in between Al-rich barrier layers in the AlGaInAsSb material system. A QD morphology might appear on the growth front, but the QD-like entities will not luminesce. Here, we use photoluminescence (PL) measurements to show that thin Al-free capsule layers between Al-rich barrier layers and the QD layers can solve this problem; this way, the QDs become optoelectronically active; that is, the dots become QDs. We consider antimonide QDs, that is, Ga(As)Sb QDs, either on GaAs for comparison or on AlxGa1-xAs barriers (x >10%) with GaAs capsule layers in between. We also discuss the influence of QD coupling both due to stress/strain from neighboring QDs and quantum-mechanically on the wavelength of the photoluminescence peak. Due to their mere existence, the capsule layers alter the barriers by becoming part of them. Quantum dots applications such as QD semiconductor lasers for spectroscopy or QDs as binary storage cells will profit from this additional degree of design freedom.

Energy optimal sizing of parallelepiped-shaped technical objects

Parallelepiped-shaped technical objects, such as tanks, storage cells, etc., are commonly used for industrial needs. Many of them require energy intensive heating. Achieving the lowest energy requirements is possible by optimizing heat losses by choosing optimal sizing of those objects. This specific case of unconditional optimization has been investigated and described in this paper. Equations for calculating optimal sizing have been suggested. The smallest surface area has been chosen as an optimization criterion while a volume of an experimental parallelepiped-shaped technical object varied. The results show that the cube shape can be taken as optimal.

Optimisation of sodium-based energy storage cells using pre-sodiation: a perspective on the emerging field

This perspective discusses how the tool of pre-sodiation can improve sodium-based electrochemical cells; the pros and cons of available pre-sodiation methods are then evaluated.