Enabling Cross-technology Communication from LoRa to ZigBee via Payload Encoding in Sub-1 GHz Bands

Low-power wireless mesh networks (LPWMNs) have been widely used in wireless monitoring and control applications. Although LPWMNs work satisfactorily most of the time thanks to decades of research, they are often complex, inelastic to change, and difficult to manage once the networks are deployed. Moreover, the deliveries of control commands, especially those carrying urgent information such as emergency alarms, suffer long delay, since the messages must go through the hop-by-hop transport. Recent studies show that adding low-power wide-area network radios such as LoRa onto the LPWMN devices (e.g., ZigBee) effectively overcomes the limitation. However, users have shown a marked reluctance to embrace the new heterogeneous communication approach because of the cost of hardware modification. In this article, we introduce LoRaBee, a novel LoRa to ZigBee cross-technology communication (CTC) approach, which leverages the energy emission in the Sub-1 GHz bands as the carrier to deliver information. Although LoRa and ZigBee adopt distinct modulation techniques, LoRaBee sends information from LoRa to ZigBee by putting specific bytes in the payload of legitimate LoRa packets. The bytes are selected such that the corresponding LoRa chirps can be recognized by the ZigBee devices through sampling the received signal strength. Experimental results show that our LoRaBee provides reliable CTC communication from LoRa to ZigBee with the throughput of up to 281.61 bps in the Sub-1 GHz bands.

Real-Time Sensing of Output Polymer Flow Temperature and Volumetric Flowrate in Fused Filament Fabrication Process

In this paper we addressed key challenges in engineering an instrumentation system for sensing and signal processing for real-time estimation of two main process variables in the Fused-Filament-Fabrication process: (i) temperature of the polymer melt exiting the nozzle using a thermocouple; and (ii) polymer flowrate using extrusion width measurements in real-time, in-situ, using a microscope camera. We used a design of experiments approach to develop response surface models for two materials that enable accurate estimation of the polymer exit temperature as a function of polymer flowrate and liquefier temperature with a fit of 𝑅2=99.96% and 99.39%. The live video stream of the deposition process was used to compute the flowrate based on a road geometry model. Specifically, a robust extrusion width recognizer algorithm was developed to identify edges of the deposited road and for real-time computation of extrusion width, which was found to be robust to filament colors and materials. The extrusion width measurement was found to be within 0.08 mm of caliper measurements with an 𝑅2 value of 99.91% and was found to closely track the requested flowrate from the slicer. This opens new avenues for advancing the engineering science for process monitoring and control of FFF.

Study on the Potential Distribution of Leptinotarsa decemlineata and Its Natural Enemy Picromerus bidens Under Climate Change

The Colorado potato beetle (CPB), scientifically known as Leptinotarsa decemlineata, is a destructive quarantine pest that has invaded more than 40 countries and regions worldwide. It causes a 20–100% reduction in plant production, leading to severe economic losses. Picromerus bidens L. is a predatory insect that preys on CPB. This study used the MaxEnt model to predict the current and future potential distribution areas of CPB and P. bidens under different climatic scenarios to determine the possibility of using P. bidens as a natural enemy to control CPB. The possible introduction routes of CPB and P. bidens were subsequently predicted by combining their potential distribution with the current distribution of airports and ports. Notably, the potential distribution area of P. bidens was similar to that of CPB, suggesting that P. bidens could be used as a natural enemy to control CPB. Future changes in the suitable growth areas of CPB under different climate scenarios increased and decreased but were insignificant, while those of P. bidens decreased. Consequently, a reduction of the suitable habitats of P. bidens may cause a decrease in its population density, leading to a lack of adequate and timely prevention and control of invasive pests. Active measures should thus be enacted to minimize global warming and protect biodiversity. This study provides a theoretical basis and data support for early warning, monitoring, and control of the CPB spread.

An investigation of the dependency of subject-verb agreement on inhibitory control processes in sentence production

Agreement attraction, i.e., the production or acceptance of a verb that agrees with a noun other than the subject of the sentence, can be viewed as a process in which conflicting cues activate competing representations. The aftermath of such competition, in terms of cognitive processes, remains unclear. Using a novel referential communication task for eliciting agreement errors and both group-level manipulation of control demands and a detailed analysis of individual differences, we provide converging evidence for the role of monitoring and inhibitory control processes in agreement attraction for singular-subject sentences. We further demonstrate the dependence of producing plural verbs on such processes, suggesting the singular form is the prepotent default form. Collectively, these findings provide a clear demonstration for the role of monitoring and control processes in agreement computations, and more generally syntactic operations in sentence production.

Application of convolutional neural networks for prediction of disinfection by-products

Fluorescence spectroscopy can provide high-level chemical characterization and quantification that is suitable for use in online process monitoring and control. However, the high-dimensionality of excitation–emission matrices and superposition of underlying signals is a major challenge to implementation. Herein the use of Convolutional Neural Networks (CNNs) is investigated to interpret fluorescence spectra and predict the formation of disinfection by-products during drinking water treatment. Using deep CNNs, mean absolute prediction error on a test set of data for total trihalomethanes, total haloacetic acids, and the major individual species were all < 6 µg/L and represent a significant difference improved by 39–62% compared to multi-layer perceptron type networks. Heat maps that identify spectral areas of importance for prediction showed unique humic-like and protein-like regions for individual disinfection by-product species that can be used to validate models and provide insight into precursor characteristics. The use of fluorescence spectroscopy coupled with deep CNNs shows promise to be used for rapid estimation of DBP formation potentials without the need for extensive data pre-processing or dimensionality reduction. Knowledge of DBP formation potentials in near real-time can enable tighter treatment controls and management efforts to minimize the exposure of the public to DBPs.

Correlation Between Insulation Resistance and Temperature Measurement Error in Type K and Type N Mineral Insulated, Metal Sheathed Thermocouples

Mineral insulated, metal sheathed (MI) Type K and Type N thermocouples are widely used in industry for process monitoring and control. One factor that limits their accuracy is the dramatic decrease in the insulation resistance at temperatures above about 600 °C which results in temperature measurement errors due to electrical shunting. In this work the insulation resistance of a cohort of representative MI thermocouples was characterised at temperatures up to 1160 °C, with simultaneous measurements of the error in indicated temperature by in situ comparison with a reference Type R thermocouple. Intriguingly, there appears to be a systematic relationship between the insulation resistance and the error in the indicated temperature. At a given temperature, as the insulation resistance decreases, there is a corresponding increasingly negative error in the temperature measurement. Although the measurements have a relatively large uncertainty (up to about 1 °C in temperature error and up to about 10 % in insulation resistance measurement), the trend is apparent at all temperatures above 600 °C, which suggests that it is real. Furthermore, the correlation disappears at temperatures below about 600 °C, which is consistent with the well-established diminution of insulation resistance breakdown effects below that temperature. This raises the intriguing possibility of using the as-new MI thermocouple calibration as an indicator of insulation resistance breakdown: large deviations of the electromotive force (emf) in the negative direction could indicate a correspondingly low insulation resistance.

Application of Mathematical Models to Determine the Feasibility of Amorphous Drug Layering in Pan Coaters

Oral solid dosage forms that contain APIs in the amorphous state have become commonplace because of many drug substances exhibiting poor water solubility, which negatively impacts their absorption in the human GI tract. While micronization, solvent spray-drying, and hot-melt extrusion can address solubility issues, spray coating of the APIs onto beads and tablets offers another option for producing amorphous drug products. High-level comparisons between bead and tablet coating technologies have the potential for simpler equipment and operation that can reduce the cost of development and manufacturing. However, spray coating directly onto tablets is not without challenges, especially with respect to meeting uniformity acceptance value (AV) criteria, comprising accuracy (mean) and precision (variance) objectives. The feasibility of meeting AV criteria is examined, based on mathematical models for accuracy and precision. The results indicate that the main difficulty in manufacturing satisfactory drug-layered tablets by spray coating is caused by the practical limitations of achieving the necessary coating precision. Despite this limitation, it is shown that AV criteria can be consistently met by appropriate materials monitoring and control as well as processing equipment setup, operation, and maintenance.

Microbial diversity in intensively farmed lake sediment contaminated by heavy metals and identification of microbial taxa bioindicators of environmental quality

The cumulative effects of anthropogenic stress on freshwater ecosystems are becoming increasingly evident and worrisome. In lake sediments contaminated by heavy metals, the composition and structure of microbial communities can change and affect nutrient transformation and biogeochemical cycling of sediments. In this study, bacterial and archaeal communities of lake sediments under fish pressure contaminated with heavy metals were investigated by the Illumina MiSeq platform. Despite the similar content of most of the heavy metals in the lagoon sediments, we found that their microbial communities were different in diversity and composition. This difference would be determined by the resilience or tolerance of the microbial communities to the heavy metal enrichment gradient. Thirty-two different phyla and 66 different microbial classes were identified in sediment from the three lagoons studied. The highest percentages of contribution in the differentiation of microbial communities were presented by the classes Alphaproteobacteria (19.08%), Cyanophyceae (14.96%), Betaproteobacteria (9.01%) y Actinobacteria (7.55%). The bacteria that predominated in sediments with high levels of Cd and As were Deltaproteobacteria, Actinobacteria, Coriobacteriia, Nitrososphaeria and Acidobacteria (Pomacocha), Alphaproteobacteria, Chitinophagia, Nitrospira and Clostridia (Tipicocha) and Betaproteobacteria (Tranca Grande). Finally, the results allow us to expand the current knowledge of microbial diversity in lake sediments contaminated with heavy metals and to identify bioindicators taxa of environmental quality that can be used in the monitoring and control of heavy metal contamination.