On Efficiently Partitioning a Topic in Apache Kafka

Apache Kafka addresses the general problem of delivering extreme high volume event data to diverse consumers via a publish-subscribe messaging system. It uses partitions to scale a topic across many brokers for producers to write data in parallel, and also to facilitate parallel reading of consumers. Even though Apache Kafka provides some out of the box optimizations, it does not strictly define how each topic shall be efficiently distributed into partitions. The well-formulated fine-tuning that is needed in order to improve an Apache Kafka cluster performance is still an open research problem. In this paper, we first model the Apache Kafka topic partitioning process for a given topic. Then, given the set of brokers, constraints and application requirements on throughput, OS load, replication latency and unavailability, we formulate the optimization problem of finding how many partitions are needed and show that it is computationally intractable, being an integer program. Furthermore, we propose two simple, yet efficient heuristics to solve the problem: the first tries to minimize and the second to maximize the number of brokers used in the cluster. Finally, we evaluate its performance via large-scale simulations, considering as benchmarks some Apache Kafka cluster configuration recommendations provided by Microsoft and Confluent. We demonstrate that, unlike the recommendations, the proposed heuristics respect the hard constraints on replication latency and perform better w.r.t. unavailability time and OS load, using the system resources in a more prudent way.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. This work was funded by the European Union's Horizon 2020 research and innovation programme MARVEL under grant agreement No 95733

Distributed Path Reconfiguration and Data Forwarding in Industrial IoT Networks

In today's typical industrial environments, the computation of the data distribution schedules is highly centralised. Typically, a central entity configures the data forwarding paths so as to guarantee low delivery delays between data producers and consumers. However, these requirements might become impossible to meet later on, due to link or node failures, or excessive degradation of their performance. In this paper, we focus on maintaining the network functionality required by the applications after such events. We avoid continuously recomputing the configuration centrally, by designing an energy efficient local and distributed path reconfiguration method. Specifically, given the operational parameters required by the applications, we provide several algorithmic functions which locally reconfigure the data distribution paths, when a communication link or a network node fails. We compare our method through simulations to other state of the art methods and we demonstrate performance gains in terms of energy consumption and data delivery success rate as well as some emerging key insights which can lead to further performance gains

5G-Based Multi-Sensor Platform for Monitoring of Workpieces and Machines: Prototype Hardware Design and Firmware

In this paper, we introduce a 5G-based multi-sensor platform for monitoring workpieces and machines. The prototype is realized within the EU-funded 5G-SMART project, which aims to enable smart manufacturing through 5G, demonstrating and validating new generation network technology in industrial processes. There are already state-of-the-art solutions, but with drawbacks such as limited flexibility, brief real-time capability, and sensors aimed at single applications. The 5G-SMART multi-sensor platform is designed to overcome these points and meet the requirements of Industry 4.0. The device is equipped with different sensors to acquire multiple data from workpieces and machines of the shop floor, wirelessly connected by 5G to the factory cloud. A hardware design description of the prototype is provided, focusing on the electronic components and their interaction with the microcontroller. Verification of the correct functioning of the board is given, with a basic library for the main peripherals used as a basis for the final firmware

PURWARUPA SISTEM PEMANTAUAN POLUSI UDARA DI RUANG TERTUTUP MENGGUNAKAN PLATFORM THINGSPEAK

Smoking is an act of negligence that a person commits intentionally and causes personal harm. The habit of smoking has spread to children and adolescents. One of the health impacts of smoking is the smoke that is released. Therefore, cigarette smoke is categorized as one of the causes of air pollution. A bad habit that smokers do is smoking in a closed room with minimal ventilation. As a result, the air in the room is contaminated by harmful substances from cigarette smoke. This study aims to monitor the quality of air exposed to cigarette smoke in a prototype closed room and measure the effectiveness of sansevieria plants placed in the room to absorb cigarette smoke in real-time. Air quality is displayed in graphical form using the Thingspeak Platform. The stages carried out in this research are air quality detected using an MQ-7 sensor integrated with the MCU8266 WiFi Node, converting sensor data into smoke density values in units of PPM (parts per million), displaying air PPM graphs in real-time and displaying the absorption ability of sansevieria against air contaminated with cigarette smoke. The results prove that one pot of sansevieria plants (5 leaves) placed in a prototype room with a size of 70cm x 30cm x 45cm can absorb cigarette smoke within 1 hour 39 minutes. While for two pots of sansevieria plants (10 leaves), it takes 1 hour and 11 minutes. Visualization of the absorption graph and normalization of air in the room can also be monitored in real-time through the Thingspeak platform based on the smoke density value against time

A peptide containing residues 26–44 of tau protein impairs mitochondrial oxidative phosphorylation acting at the level of the adenine nucleotide translocator

n/

correction novel chemical probes for the investigation of nonribosomal peptide assembly

Correction for 'Novel chemical probes for the investigation of nonribosomal peptide assembly' by Y. T. Candace Ho et al., Chem. Commun., 2017, 53, 7088–7091

Glutathione-triggered disassembly of isothermally responsive polymer nanoparticles obtained by nanoprecipitation of hydrophilic polymers

The encapsulation and selective delivery of therapeutic compounds within polymeric nanoparticles offers hope for the treatment of a variety of diseases. Traditional approaches to trigger selective cargo release typically rely on polymer degradation which is not always sensitive to the biological location of a material. In this report, we prepare nanoparticles from thermoresponsive polymers with a ‘solubility release catch’ at the chain-end. This release catch is exclusively activated in the presence of intracellular glutathione, triggering an ‘isothermal’ response and promoting a change in polymer solubility. This solubility switch leads to specific and rapid nanoparticle disassembly, release of encapsulated cargo and produces completely soluble polymeric side-products

Requirement of a Membrane Potential for the Posttranslational Transfer of Proteins into Mitochondsria

Posttranslational transfer of most precursor proteins into mitochondria is dependent on energization of the mitochondria. Experiments were carried out to determine whether the membrane potential or the intramitochondrial ATP is the immediate energy source. Transfer in vitro of precursors to the ADP/ATP carrier and to ATPase subunit 9 into isolated Neurospora mitochondria was investigated. Under conditions where the level of intramitochondrial ATP was high and the membrane potential was dissipated, import and processing of these precursor proteins did not take place. On the other hand, precursors were taken up and processed when the intramitochondrial ATP level was low, but the membrane potential was not dissipated. We conclude that a membrane potential is involved in the import of those mitochondrial precursor proteins which require energy for intracellular translocatio

Nanolipid-trehalose conjugates and nano-assemblies as putative autophagy inducers

The disaccharide trehalose is an autophagy inducer, but its pharmacological application is severely limited by its poor pharmacokinetics properties. Thus, trehalose was coupled via suitable spacers with squalene (in 1:2 and 1:1 stoichiometry) and with betulinic acid (1:2 stoichiometry), in order to yield the corresponding nanolipid-trehalose conjugates 1-Sq-mono, 2-Sq-bis and 3-Be-mono. The conjugates were assembled to produce the corresponding nano-assemblies (NAs) Sq-NA1, Sq-NA2 and Be-NA3. The synthetic and assembly protocols are described in detail. The resulting NAs were characterized in terms of loading and structure, and tested in vitro for their capability to induce autophagy. Our results are presented and thoroughly commented upon