Layer‐by‐Layer All‐Inorganic Quantum‐Dot‐Based LEDs: A Simple Procedure with Robust Performance

A novel all‐inorganic electroluminescent device is demonstrated based on highly luminescent CdTe nanocrystals intercalated within a laminar hydrotalcite‐like structure. The laminar scaffold acts to both support and distribute the CdTe nanocrystals. The device is synthesized using simple wet chemical processes at room temperature in ambient conditions. It has high thermal stability, operating continuously up to 90 °C, and a maximum efficiency at J = 0.12 A cm^?2. The device is targeted at the automotive industry.     

Securing light clients in blockchain with DLCP

In blockchain, full nodes (FNs) are peers that store and verify entire chains of transactions. In contrast, light clients (LCs) are those with limited resources, and for this reason, they request only block headers from FNs for transaction verification—using protocols like Simple Payment Verification (SPV). In an approach to prevent FN tampering on transaction verification (byzantine fault), LCs request block headers from multiple FNs and compare received responses. One problem with this approach is that an LC must connect to each FN and perform the same cryptographic operations with each one repeatedly, which leads to client‐side complexity and slower response. We propose an alternate approach to tackle this issue, in which LCs can encrypt a request for block headers only once, and send that request to a predetermined set of FNs to access, process, and reply back in a single response. Our approach, called Distributed Lightweight Client Protocol (DLCP), enables LCs to verify with little effort if FNs have agreed on a response. From an experimental evaluation, we observed that DLCP provided lower latency and reduced computing and communication overhead in comparison with the existing conventional approach.     

Manipulation of Training Sets for Improving Data Mining Coverage-Driven Verification

The constant pressure for making functional verification more agile has led to the conception of coverage driven verification (CDV) techniques. CDV has been implemented in verification testbenches using supervised learning techniques to model the relationship between coverage events and stimuli generation, providing a feedback between them. One commonly used technique is the classification- or decision-tree data mining, which has shown to be appropriate due to the easy modeling. Learning techniques are applied in two steps: training and application. Training is made on one or more sets of examples, which relate datasets to pre-determined classes. Precision of results by applying the predictive learning concept has shown to be sensitive to the size of the training set and the amount of imbalance of associated classes, this last meaning the number of datasets associated to each class is very different from each other. This work presents experiments on the manipulation of data mining training sets, by changing the size and reducing the imbalances, in order to check their influence on the CDV efficiency. To do that, a circuit example with a large input space and strong class imbalance was selected from the application domain of multimedia systems and another one, with a small input space that affects the coverage occurrences, was selected from the communication area.     

A genetic approach to joint routing and link scheduling for wireless mesh networks

Wireless mesh networks are an attractive technology for providing broadband connectivity to mobile clients who are just on the edge of wired networks, and also for building self-organized networks in places where wired infrastructures are not available or not deemed to be worth deploying. This paper investigates the joint link scheduling and routing issues involved in the delivery of a given backlog from any node of a wireless mesh network towards a specific node (which acts as a gateway), within a given deadline. Scheduling and routing are assumed to be aware of the physical interference among nodes, which is modeled in the paper by means of a signal-to-interference ratio. Firstly, we present a theoretical model which allows us to formulate the task of deriving joint routing and scheduling as an integer linear programming problem. Secondly, since the problem cannot be dealt with using exact methods, we propose and use a technique based on genetic algorithms. To the best of our knowledge, these algorithms have never been used before for working out these kinds of optimization problems in a wireless mesh environment. We show that our technique is suitable for this purpose as it provides a good trade-off between fast computation and the overall goodness of the solution found. Our experience has in fact shown that genetic algorithms would seem to be quite promising for solving more complex models than the one dealt with in this paper, such as those including multiple flows and multi-radio multi-channels.     

Nanotechnology‐Enabled Closed Loop Insulin Delivery Device: In Vitro and In Vivo Evaluation of Glucose‐Regulated Insulin Release for Diabetes Control

Recently, a new multifunctional, bio‐inorganic nanocomposite membrane with the ability to self‐regulate the release of insulin in response to blood glucose (BG) levels was reported. Herein, the application of this material as part of a small, implantable, closed‐loop insulin delivery device designed to continuously monitor BG concentrations and regulate insulin release is proposed. The insulin delivery device consists of a nanocomposite glucose‐responsive plug covalently bound to an insulin reservoir made of surface‐modified silicone. The plug is prepared with crosslinked bovine serum albumin (BSA) and enzymes (glucose oxidase (GOx) and catalase (CAT)), pH‐responsive hydrogel nanoparticles, and multifunctional MnO₂ nanoparticles. The plug functions both as a glucose sensor and controlled delivery unit to release higher rates of insulin from the reservoir in response to hyperglycemic BG levels and basal insulin rates at normal BG concentration. The surfaces of the device are modified by silanization followed by PEGylation to ensure its safety and biocompatibility and the stability of encased insulin. Our results show that insulin release can be modulated in vitro in response to glucose concentrations. In vivo experiments show that the glycemia of diabetic rats can be controlled with implantation of the prototype device. The glucose‐responsiveness of the device is also demonstrated by rapid drop in BG level after challenging diabetic rats with bolus injection of glucose solution. In addition, it is demonstrated that surface PEGylation of the device is necessary for reducing the immune response of the host to the implanted foreign object and maintaining insulin stability and bioactivity. With this molecular architecture and the bio‐inorganic nanocomposite plug, the device has the ability to maintain normal BG levels in diabetic rats.     

Diagnosis of learner dropout based on learning styles for online distance learning

The amount of data generated by computer systems in Online Distance Learning (ODL) contains rich information. One example of this information we define as the Learner Learning Trail (LLT), which is the sequence of interactions between the students and the virtual environment. Another example is the Learner Learning Style (LLS), which is associated with the student behavior and choices during the learning process. This information can be used to identify learner behavior and learning style. We perceived, after the study of related literature, that the research field of learner diagnosis for ODL does not apply the conjoint use of LLT and LLS. In this article, we propose a model capable of integrating data generated from the behavior of students in ODL with cognitive aspects of them, such as their Learning Styles, by crossing LLT with LLS. We also propose the CPAD method (Collect, Preprocessing, Analysis, Diagnosis), which is implemented by collecting the raw data regarding learning activities, preprocessing the data into structured time sequences, analyzing the sequences regarding the learning styles and using this analysis to diagnose the learner behavior. We selected the dropout to investigate, once the dropout rate in ODL is a real problem in universities around the world. In addition, the dropout is a student decision which can be associated with previous students behaviors. We performed a study with 202 learners to evaluate if learning styles are capable of explaining aspects of the student behavior. The results suggest that Sequential/Global learning style dimension is more capable of explaining the dropout than the other dimensions. Also, we performed four classification experiments to verify how the dimensions of Felder-Silverman Learning Style Model influence the learner diagnosis. We perceived that the Sequential/Global dimension could provide a higher accuracy average with lower variation independently of the diagnosis technique.     

Tunable Nanoparticle and Cell Assembly Using Combined Self‐Powered Microfluidics and Microcontact Printing

The combination of cell microenvironment control and real‐time monitoring of cell signaling events can provide key biological information. Through precise multipatterning of gold nanoparticles (GNPs) around cells, sensing and actuating elements can be introduced in the cells' microenviroment, providing a powerful substrate for cell studies. In this work, a combination of techniques are implemented to engineer complex substrates for cell studies. Alternating GNPs and bioactive areas are created with micrometer separation by means of a combination of vacumm soft‐lithography of GNPs and protein microcontract printing. Instead of conventional microfluidics that need syringe pumps to flow liquid in the microchannels, degas driven flow is used to fill dead‐end channels with GNP solutions, rendering the fabrication process straightforward and accessible. This new combined technique is called Printing and Vacuum lithography (PnV lithography). By using different GNPs with various organic coating ligands, different macroscale patterns are obtained, such as wires, supercrystals, and uniformly spread nanoparticle layers that can find different applications depending on the need of the user. The application of the system is tested to pattern a range of mammalian cell lines and obtain readouts on cell viability, cell morphology, and the presence of cell adhesive proteins.     

Methylmercury Causes Neurodegeneration and Downregulation of Myelin Basic Protein in the Spinal Cord of Offspring Rats after Maternal Exposure

Methylmercury (MeHg) is one of the most dangerous toxic pollutants spread throughout the earth. Chronic MeHg intoxication by contaminated food ingestion is the most common threat to human health, including impairment to the developing fetus. The present study aims at investigating the effects of maternal exposure to MeHg during gestation and lactation on the spinal cord of offspring. Pregnant rats received oral doses of MeHg (40 μg/kg/day) over a period of 42 days (21 gestation and 21 lactation). Control animals received the vehicle only. Total mercury concentration was measured in blood samples from offspring collected at the 41st postnatal day. Counting of motor neurons and immunoreactivity for myelin basic protein (MBP) were assessed in the spinal cords in both control and MeHg-intoxicated animals. Our results showed that MeHg promoted an increase in blood Hg levels. In addition, it caused a reduction in the number of spinal cord motor neurons as well as decreased MBP immunoreactivity in the cervical, thoracic and lumbar segments. Our present findings suggest that MeHg intoxication during rat pregnancy and lactation is associated with a pattern of motor neuron degeneration and downregulation of myelin basic protein in different segments of a developing spinal cord. Further studies are needed to establish the effect of MeHg intoxication in both young and adult rats.