Two Shades of Grey: Effect of Temperature on Seed Germination of the Escaping Ornamental Species Lychnis Coronaria and Stachys Byzantina

This paper describes germination and cytological variability in two popular ornamental and potentially invasive species, lamb’s ear Stachys byzantina and rose campion Lychnis coronaria. Both xerophytic species have the potential to invade natural habitats across Europe and create viable naturalised populations, with subsequent impacts on native vegetation. To assess the species’ invasiveness, seeds were collected from naturalised populations and germination rate recorded under different temperature regimes. Flow cytometry, used to record cytological variability, indicated that all populations of both species were cytologically homogeneous. Germination success, a key spreading factor in both species, was significantly influenced by temperature, with final germination of L. coronaria being extremely high at temperatures > 15/10 °C (98.5%) and extremely low at temperatures < 10/5 °C (2.9%). In comparison, final germination in S. byzantina highest at 22/15 °C (55.6%), reducing to 40.3% at 15/10 °C and just 0.3% at temperatures < 10/5 °C. No significant differences in germination rate were observed between escaping and non-escaping populations, though there were differences between particular populations. Our results indicate germination temperature limits between species consistent with sizes of primary distribution and distance between primary and secondary distribution borders. However, the observed germination rates allow for successful generative reproduction of both species over their secondary distribution areas, suggesting that these species are likely to become invasive species of European grasslands soon.

Investigation of Dynamic Characteristics of Liquid Nitrogen Droplet Impact on Solid Surface

Liquid nitrogen spray cooling technology exhibits excellent heat transfer efficiency and environmental protection performance. The promotion of this technology plays an important role in improving the sustainable development of the refrigeration industry. In order to clarify its complex microscale behavior, the coupled Level Set-VOF method was adopted to study the dynamic characteristics of liquid nitrogen droplet impact on solid surface in this paper. The spreading behaviors under various factors (initial velocity, initial diameter, wall temperature, and We number) were systematically analyzed. The results show that the spreading behaviors of liquid nitrogen droplet share the same process with the normal medium, which are rebound, retraction, and splashing. For the droplet with smaller velocity and diameter, Rebound is the common phenomenon due to the smaller kinetic energy. With the increase of droplet diameter (0.2 mm to 0.5 mm) and velocity (0.1 m/s to 5 m/s), the spreading factor increases rapidly and the spreading behaviors evolve into retraction and splashing. The increase of wall temperature accelerates the droplets spreading, and the spreading factor increases accordingly. For the liquid nitrogen droplets hit the wall, the dynamic behaviors of rebound (We < 0.2), retraction (0.2 < We < 4.9), and splashing (We > 4.9) will occur with the droplet weber number increased, which are consistent with the common medium. However, due to liquid nitrogen having lower viscosity and surface tension, the conditions of morphological transformations are different from the common media. The maximum spreading diameter has a power correlation with We, the power index of We is 0.306 for liquid nitrogen, lager than common medium (0.25). The reasons are: (1) the better wettability of liquid nitrogen, and (2) the vapor generated by the violent phase change ejects along the axial direction. The article will provide a certain theoretical basis for liquid nitrogen spray cooling technology, and can also enrich the flow dynamics of cryogenic fluids.

A Spreading Factor Congestion Status-Aware Adaptive Data Rate Algorithm

LoRaWAN has established itself as one of the leading MAC layer protocols in the field of LPWAN. Although the technology itself is quite mature, its resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. Recent studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors proposed a spreading factor congestion status aware ADR version and compared its performance against that of four other related algorithms to study the performance improvements the algorithm brings to LoRaWAN in terms of DER and EC. LoRaSim was used to evaluate the algorithms’ performances in a simple sensing application that involved end devices transmitting data to the gateway every hour. The performances were measured based on how they affected DER as the network size increases. The results obtained show that the proposed algorithm outperforms the currently existing implementations of the ADR in terms of both DER and EC. However, the proposed algorithm is slightly outperformed by the native ADR in terms of EC. This was expected as the algorithm was mainly built to improve DER. The proposed algorithm builds on the existing algorithms and the ADR and significantly improves them in terms of DER and EC (excluding the native ADR), which is a significant step towards an ideal implementation of LoRaWAN’s ADR.

The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions

LoRaWAN has become a popular technology for the Internet of Things (IoT) device connectivity. One of the expected properties of LoRaWAN is high network scalability. However, LoRaWAN network performance may be compromised when even a relatively small number of devices use link-layer reliability. After failed frame delivery, such devices typically tend to reduce their physical layer bit rate by increasing their spreading factor (SF). This reaction increases channel utilization, which may further degrade network performance, even into congestion collapse. When this problem arises, all the devices performing reliable frame transmission end up using SF12 (i.e., the highest SF in LoRaWAN). In this paper, we identify and characterize the described network condition, which we call the SF12 Well, in a range of scenarios and by means of extensive simulations. The results show that by using alternative SF-management techniques it is possible to avoid the problem, while achieving a packet delivery ratio increase of up to a factor of 4.7.

МЕТОД ГЛУБОКОГО ОБУЧЕНИЯ С ПОДКРЕПЛЕНИЕМ ДЛЯ СИСТЕМ ИНТЕРНЕТА ВЕЩЕЙ НА БАЗЕ ТЕХНОЛОГИИ LORA С ОГРАНИЧЕННЫМИ РЕСУРСАМИ И ПОДДЕРЖКОЙ QoS

Achieving high Quality of Service (QoS) remains a challenge for LoRa technology. However, high QoS can be achieved via optimizing the transmission policy parameters such as bandwidth and code rate. Existing approaches do not provide an opportunity to optimize the LoRa networks' data transmission parameters. The article proposes transmission policy enforcementfor QoS-aware LoRanetworks.The QoSparameter ranking is implemented for IoT nodes where priority and nonpriority information is identified by the new field of LoRa frame structure(QRank).The optimaltransmissionpolicyenforcement uses fast deep reinforcement learning that utilizes the environmental parameters including QRank, signal quality, and signal-to-interference-plus-noise-ratio. The transmission policy is optimized for spreading factor, code rate, bandwidth, and carrier frequency. Performance evaluation is implemented using an NS3.26 LoRaWAN module. The performance is examined for various metrics such as delay and throughput. Достижение высокого качества обслуживания (QoS) по-прежнему остается достаточно сложной задачей для технологии LoRa. В принципе высокий уровень QoS может быть достигнут за счет оптимизации параметров передачи, например, пропускной способности и скорости передачи информации в сети. Известные на сегодняшний день решения не дают возможности оптимизировать параметры передачи данных для сетей LoRa. В статье предложен эффективный метод передачи данных, обеспечивающий требования по QoS при использовании технологии LoRa. Ранжирование параметров QoS для узлов интернета вещей определяется новым полем структуры фрейма LoRa (QRank) для приоритетной и неприоритетной информации. Для обеспечения эффективной передачи применяется быстрое глубокое обучение с подкреплением, для которого используются как параметры качества обслуживания, так и отношение сигнал/шум. Метод передачи оптимизирован с учетом коэффициента распространения, скорости передачи данных, полосы пропускания и несущей частоты. Оценка производительности при применении предложенного метода проведена с использованием модуля LoRaWAN в пакете имитационного моделирования NS3.26. Производительность оценивается на основе параметров задержки и пропускной способности.

Waste Bread as Main Ingredient for Cookie Elaboration

Food waste is a current global problem. The aim of this work was to investigate the possibility of reintroducing bread discarded by retailers in the preparation of sugar-snap cookies. Bread flours were obtained from stale breads (white and whole wheat) milled with 200, 500 and 1000 μm sieves. Cookies were elaborated using 100% bread flours and combinations of 50% of bread flour and wheat flour. The rheology of the doughs, the dimensions of the cookies, their texture and colour were evaluated. Bread flour doughs presented higher G’ (elastic modulus), G” (viscous modulus) values than the control, especially with increased particle size. Bread flour cookies had a smaller diameter and a harder texture than the control, but in the case of whole bread flours of larger particle sizes, those differences were reduced. Cookies made with bread flour had a darker colour and higher a* values. The 50% mixtures did not present significant differences with respect to the control in terms of dough rheology, hardness, or lightness. Although the spreading factor was reduced, it was more similar to the control than to 100% bread flour cookies. Wasted bread flour can thus be used to replace wheat flour in cookie formulations.

Designing LoRaWAN Network For Unbiased Communication Between Nodes and Gateway

Proper cell designing is required to achieve a target system performance in Long Range Wide Area Network (LoRaWAN). This paper addresses a suitable selection of network designing parameters problem, such as the dimension of different spreading factors’ annuli (zones or SF boundaries) in a LoRaWAN cell. We propose a mathematical framework for designing the LoRaWAN network. The main objective is to ensure that distributed end devices in the network can have the same success probability, irrespective of the spreading factor usage and their locations, unlike Equal Area Based (EAB) based network. We further enhance the performance of the network based on the proposed dimensions by adopting the k-tolerance algorithm. When the network follows the proposed dimensions, simulation results show an improvement in overall success probability over the traditional EAB scheme. In the later part of the paper, we address urbanization issues that degrade the system performance. In our approach to recoup the degradation in the system performance, we implement k−tolerance algorithm in the network.

A Comparative Study of Machine Learning Models for Spreading Factor Selection in LoRa Networks

Low power wide area networks (LPWAN) technologies offer reasonably priced connectivity to a large number of low-power devices spread over great geographical ranges. Long range (LoRa) is a LPWAN technology that empowers energy-efficient communication. In LoRaWAN networks, collisions are strongly correlated with spreading factor (SF) assignment of end-nodes which affects network performance. In this work, SF assignment using machine learning models in simulation environment is presented. This work examines three approaches for the selection of the SF during LoRa transmissions: 1) random SF assignment, 2) adaptive data rate (ADR), and 3) SF selection through machine learning (ML). The main target is to study and determine the most efficient approach as well as to investigate the benefits of using ML techniques in the context of LoRa networks. In this research, a library that enables the communication between ML libraries and OMNeT++ simulator was created. The performance of the approaches is evaluated for different scenarios using the delivery ratio and energy consumption metrics.

Concatenative Complete Complementary Code Division Multiple Access and its Fast Transform based Implementation

Over multipath channels, \textit{complete complementary code division multiple access} (CC-CDMA) and \textit{convolutional spreading code division multiple access} (CS-CDMA) provide {\it inter-channel interference} (ICI) free transmission with an enhanced {\it spectral efficiency} (SE). However, the {\it convolutional spreading} (CS) operation of the systems is computationally complex and involves a high \textit{peak-to-average power ratio} (PAPR). Address such issues, we propose the \textit{concatenative complete complementary code division multiple access} (CCC-CDMA). Since the CCCCs can be generated from the rows of the Walsh-Hadamard or \textit{discrete Fourier transform} (DFT) matrices, the CS operation can be implemented using corresponding {\it fast transforms} (FTs) to reduce computational complexity. Simulation results shown that enlargement of {\it spreading factor} (SF) strengthens the robustness against clipping noise and the binary CCCC generated by Walsh-Hadamard matrix has excellent robustness against Doppler frequency shifts.