Search

Combining pulses and cereals allows our body to have all the essential amino acids (in lysines and methionines) and helps strengthen the fiber content through various minerals and vitamins of group B. This combination allows obtaining a protein assimilation rate higher by 30 to 50% than consuming it separately. The aim of this study is to compare the quality of a pulse-enriched couscous (bean, chickpea and lentil) to 30% and 50% with a couscous made from 100% wheat. The comparison was based on physical tests (moisture, water activity, swelling index and color index), biochemical tests (dietary fiber, protein levels, ashes, fat and carbohydrates) and microbiological tests (total germs, yeasts and molds). The results obtained showed that pulses couscous is more interesting from a conservation point of view (Aw = 0.52) compared to the one based on 100% wheat (Aw = 0.62). The nutritional quality of pulses-based couscous (ash content, protein and dietary fiber) is more appreciable than that of durum couscous. The more couscous is rich in pulses the more its nutritional profile is important (less fat and carbohydrates). Only couscous reinforced with chickpea is less rich in protein (10.21g/100g MS) than that based on wheat (12.10g/100g MS). On a microbiological level, we also noticed that the more reinforced couscous is in pulses the more its microbial load goes down either for the total germs or for the yeasts and molds.

Accurate crop development modelling is important in planning rational use of water resources and crop management, along with evaluating the effects of climate change on crop productivity and predicting yields. This study aims at modelling winter wheat productivity under different climatic scenarios and models, in a Moroccan rainfed agriculture site using the FAO AquaCrop model. The study site was a one-hectare experiment plot located in the Marchouch plateau in the North-western Morocco. The model was calibrated using field parameters of the crop growing cycle, the soil characteristics, the crop management and the observed yield for a period covering three cropping cycles (from 2014/2015 to 2016/2017) using daily rainfall and temperature data. The calibrated model was then used to simulate wheat productivity in the study site for short-term (2020-2030) and medium-term periods (2040-2050) compared to a reference (1985-2005). Two Representative Concentration Pathway scenarios (RCP 4.5 and RCP 8.5) were considered for three General circulation models (CNRM, EC-EARTH, and GFDL) to derive the average model outputs with focus on the crop yield, crop growing cycle and evapotranspiration. The results showed a good model calibration, with coefficient of determination R2 of 0.84, Nash-Sutcliffe indicator of 0.71 and Willmott index of 0.94. Simulations showed that under RCP 4.5 a short-term yield drop of 5.94%, a medium-term crop cycle decrease of 15 days and evapotranspiration reduction of 20mm. Meanwhile, the model predicted under RCP 8.5 a medium-term yield drop of 12.9%, a crop cycle decrease of 13 days and evapotranspiration reduction of 46mm. Overall, simulation results showed that AquaCrop model is suitable for simulating the effects of water and climatic stress on crop productivity in rainfed agricultural areas, which could help decision making in terms of water productivity and crop adaptation under future climate trends in the semiarid conditions.

iBeacon systems have been increasingly established in public areas to assist users in terms of indoor location navigation and positioning. People receive the services through the Bluetooth Low Energy (BLE) installed on their mobile phones. However, the positioning and navigation functions of an iBeacon system may be compromised when faced with cyberattacks issued by hackers. In other words, its security needs to be further considered and enhanced. This study took the iBeacon system of Taipei Main Station, the major transportation hub with daily traffic of at least three hundred thousand passengers, as an example for exploring its potential attacks and further studying the defense technologies, with the assistance of AI techniques and human participation. Our experiments demonstrate that in the early stage of iBeacon system information security planning, information security technology and a rolling coding encryption should be included, representing the best defense methods at present. In addition, we believe that the adoption of rolling coding is the most cost-effective defense. However, if the security of critical infrastructure is involved, the most secure defense method should be adopted, namely a predictable and encrypted rolling coding method. [ABSTRACT FROM AUTHOR]

Due to the rising number of aged people in the last decades, many apprehensions about their quality of life and their personal security in their own homes came to surface especially when these persons prefer to live alone. Compared to other age groups, old persons are more susceptive to domestic incidents, such as sudden illnesses and falls. Therefore, using new technologies in elderly homecare became one of the most urgent issues to study. These technologies are classified into three categories: real time vital sign monitoring, activity recognition and indoor localization. In this paper we present our work in indoor positioning which is the combination of three existing technologies in a more effective, precise and accurate way: Bluetooth Low Energy, Acoustic and Light Fidelity (LiFi). Our main contribution is the proposition of new "dilution of precision" (DOP) indicators as precision weights of the results given by each technology. Depending on these new DOP-like indicators, we were able to judge how much precise and accurate the returned results are. The experiments we conducted proved that precision is much better when using these new precision metrics. [ABSTRACT FROM AUTHOR]

Smart cities and smart mobility necessitate the development of newly adapted approaches and applications that ensure data connection among their particular parts; thus, the complex systems of smart technologies can be applied. Especially in the field of transport, mobility, and smart solutions, many of these applications require precise localization. Global navigation satellite system (GNSS) localization is used mainly because of its availability, reliability, and sufficient precision. However, there are also blind spots in smart cities where the GNSS is not available, e.g., in tunnels. This article presents an approach to fixing the issue of localization in road tunnels based on telecommunications with Bluetooth low energy (BLE) based infrastructure equipment. This approach enables us to ensure sufficient data resources for localization, and thus, smart applications can also be applied in the tunnels. At the same time, this approach also allows the fulfillment of individual needs according to the specifications of each tunnel or smart application. The outputs from testing and measurements are presented and discussed. [ABSTRACT FROM AUTHOR]

COVID-19 is highly contagious and spreads rapidly; it can be transmitted through coughing or contact with virus-contaminated hands, surfaces, or objects. The virus spreads faster indoors and in crowded places; therefore, there is a huge demand for contact tracing applications in indoor environments, such as hospitals and offices, in order to measure personnel proximity while placing as little load on them as possible. Contact tracing is a vital step in controlling and restricting pandemic spread; however, traditional contact tracing is time-consuming, exhausting, and ineffective. As a result, more research and application of smart digital contact tracing is necessary. As the Internet of Things (IoT) and wearable sensor device studies have grown in popularity, this work has been based on the practicality and successful implementation of Bluetooth low energy (BLE) and radio frequency identification (RFID) IoT based wireless systems for achieving contact tracing. Our study presents autonomous, low-cost, long-battery-life wireless sensing systems for contact tracing applications in hospital/office environments; these systems are developed with off-the-shelf components and do not rely on end user participation in order to prevent any inconvenience. Performance evaluation of the two implemented systems is carried out under various real practical settings and scenarios; these two implemented centralised IoT contact tracing devices were tested and compared demonstrating their efficiency results. [ABSTRACT FROM AUTHOR]

With the advent of the Internet of Things (IoT) era, a wide array of wireless sensors supporting the IoT have proliferated. As key elements for enabling the IoT, wireless sensor nodes require minimal energy consumption and low device complexity. In particular, energy-efficient resource scheduling is critical in maintaining a network of wireless sensor nodes, since the energy-intensive processing of wireless sensor nodes and their interactions is too complicated to control. In this study, we present a practical deep Q-network (DQN)-based packet scheduling algorithm that coordinates the transmissions of multiple IoT devices. The scheduling algorithm dynamically adjusts the connection interval (CI) and the number of packets transmitted by each node within the interval. Through various experiments, we verify how effectively the proposed scheduler improves energy efficiency and handles the time-varying nature of the network environment. Moreover, we attempt to gain insight into the optimized packet scheduler by analyzing the policy of the DQN scheduler. The experimental results show that the proposed scheduling algorithm can further prolong a network's lifetime in a dynamic network environment in comparison with that in other alternative schemes while ensuring the quality of service (QoS). [ABSTRACT FROM AUTHOR]

This study aims to document the renewal of an import-substitution food policy in Cameroon. It examines how the production of bread flour from local food crops like cassava, plantain and sweet potato can become competitive with imported wheat. This study uses a food chain and cluster analysis framework, coupled with primary and secondary databases and surveys. The results confirm the trend towards wheat imports, with risks on future food sovereignty. However, it will be difficult to focus solely on the objective of substituting imported flour. Domestic production of cassava, plantain and sweet potato still provides little surplus to meet the needs of industrial production of bread flour. Nevertheless, these results confirm the opportunity to develop clusters made up of small processing units that add value to a diversity of local products from tropical starch products. [ABSTRACT FROM AUTHOR]

In the digital age, people may not be able to live without smart devices because they easily adopt them. IoT has made smart devices possible, which consist of sensors, connectivity, the cloud, and user interfaces. IoT research places sensors at the forefront of smart devices. IoT sensors are efficiently used in different IoT applications to develop smart environments in all fields. These sensors act like human sensing organs that collect real-time information from physical objects or the environment and convert it into electronic signals for processing in the IoT. Normally, two types of networks are applied in IoT: long-range low-power wide area networks and short-range networks. This review aims to assist researchers in understanding IoT applications and protocol classifications using short-range and long-range wireless technology that can communicate wirelessly within regions that facilitate IoT communication from a minimum distance of one millimeter to a few kilometers. This paper presents the types of sensors and various applications of IoT, as well as the energy requirements of different protocols and applications. Each network type has its own unique characteristics regarding energy consumption in the IoT network. [ABSTRACT FROM AUTHOR]

In indoor positioning problems, GPS technology used in outdoor positioning needs to be improved due to the characteristic features of wireless signals. There currently needs to be a generally accepted standard method for indoor positioning. In this study, an ecosystem consisting of Beacon devices, Bluetooth intelligent devices, and Wi-Fi access points has been created to propose an effective indoor location determination method by using Wi-Fi and BLE technologies in a hybrid way. First, RSSI (Received Signal Strength Indicator) data were collected using the fingerprint method. Then, Kalman Filter and Savitzky Golay Filter are used in a hybrid manner to reduce the noise on the obtained signal data and make it more stable. In the first part, using the collected data from Wi-Fi and Beacon devices, the Non-linear least squares method (NLLS), including Levenberg-Marquardt (LM), is used for indoor tracking. In the second part, a fingerprinting-based approach is tested. K Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms estimate the area where the client is located. Each algorithm's accuracy rate are calculated on different training and test data and presented. [ABSTRACT FROM AUTHOR]

In the automotive field, the introduction of keyless access systems is revolutionizing car entry techniques currently dominated by a physical key. In this context, this paper investigates the possible use of smartphones to create a PEPS (Passive Entry Passive Start) system using the BLE (Bluetooth Low-Energy) Fingerprinting technique that allows, along with a connection to a low-cost BLE micro-controllers network, determining the driver's position, either inside or outside the vehicle. Several issues have been taken into account to assure the reliability of the proposal; in particular, (i) spatial orientation of each microcontroller-based BLE node which ensures the best performance at 180° and 90° referred to as the BLE scanner and the advertiser, respectively; (ii) data filtering techniques based on Kalman Filter; and (iii) definition of new network topology, resulting from the merger of two standard network topologies. Particular attention has been paid to the selection of the appropriate measurement method capable of assuring the most reliable positioning results by means of the adoption of only six embedded BLE devices. This way, the global accuracy of the system reaches 98.5%, while minimum and maximum accuracy values relative to the individual zones equal, respectively, to 97.3% and 99.4% have been observed, thus confirming the capability of the proposed method of recognizing whether the driver is inside or outside the vehicle. [ABSTRACT FROM AUTHOR]

In this paper, we compare the direct TOA-based UWB technology with the RSSI-based BLE technology using machine learning algorithms for proximity detection during epidemics in terms of complexity of implementation, availability in existing smart phones, and precision of the results. We establish the theoretical limits on the precision and confidence of proximity estimation for both technologies using the Cramer Rao Lower Bound (CRLB) and validate the theoretical foundations using empirical data gathered in diverse practical operating scenarios. We perform our empirical experiments at eight distances in three flat environments and one non-flat environment encompassing both Line of Sight (LOS) and Obstructed-LOS (OLOS) situations. We also analyze the effects of various postures (eight angles) of the person carrying the sensor, and four on-body locations of the sensor. To estimate the range with BLE RSSI, we use 14 features for training the Gradient Boosted Machines (GBM) learning algorithm and we compare the precision of results with those obtained from memoryless UWB TOA ranging algorithm. We show that the memoryless UWB TOA algorithm achieves 93.60% confidence, slightly outperforming the 92.85% confidence of the BLE RSSI with more complex GBM machine learning (ML) algorithm and the need for substantial training. The training process for the RSSI-based BLE social distance measurements involved 3000 measurements to create a training dataset for each scenario and post-processing of data to extract 14 features of RSSI, and the ML classification algorithm consumed 200 s of computational time. The memoryless UWB ranging algorithm achieves more robust results without any need for training in less than 0.5 s of computation time. [ABSTRACT FROM AUTHOR]

Smart Healthcare can use the Internet of Things (IoT) to broaden the reach of digital healthcare by collecting patient data remotely using sensors. This can reduce Patient Turnaround Time (PTAT) and enable high-quality care to be provided. PTAT is the length of time from when a patient arrives at the hospital until they are allowed to return home. Malaysia's Ministry of Health claimed in 2016 that healthcare at government hospitals continues to encounter issues in providing high-quality care to patients, particularly in terms of the PTAT of patients who receive treatment versus those who are sent home without treatment. In this paper, we propose a Bluetooth Low Energy-based solution that optimizes PTAT using low calibrated transmission power, allowing hospitals to enable Real-time Patient Localization and Patient Movement Monitoring. The RSSI value is used to calculate the distance between a wearable device and the Access Points (AP) situated throughout the facility. When a patient passes an AP, data such as the wearable device name and RSSI value are taken and saved in a database, to determine the patient's location. A proof of concept was conducted using three AP points and 8 wearable devices to gauge distance measurement. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Plant Science is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The beta regression model has become a popular tool for assessing the relationships among chemical characteristics. In the BRM, when the explanatory variables are highly correlated, then the maximum likelihood estimator (MLE) does not provide reliable results. So, in this study, we propose a new modified beta ridge-type (MBRT) estimator for the BRM to reduce the effect of multicollinearity and improve the estimation. Initially, we show analytically that the new estimator outperforms the MLE as well as the other two well-known biased estimators i.e., beta ridge regression estimator (BRRE) and beta Liu estimator (BLE) using the matrix mean squared error (MMSE) and mean squared error (MSE) criteria. The performance of the MBRT estimator is assessed using a simulation study and an empirical application. Findings demonstrate that our proposed MBRT estimator outperforms the MLE, BRRE and BLE in fitting the BRM with correlated explanatory variables.

Estimating the distance between two smartphones plays an important role in a host of applications. For this purpose, smartphones emit and scan for Bluetooth Low Energy (BLE) signals. When a device is detected, the distance is estimated by evaluating the received strength of these signals. The main insight that is exploited for distance estimation is that the attenuation of a signal increases with the distance along which it has traveled. However, besides distance, there are multiple additional factors that impact the attenuation and hence disturb the distance estimation procedure. Among them, frequency-selective hardware and signal propagation belong to the most significant ones. For example, a BLE device transmits packets on three different frequencies (channels), while the transmit power and the receiver sensitivity depend on the frequency. As a result, the received signal strength varies for each channel, even when the distance remains constant. However, the information on which wireless channel a packet has been received is not made available to a smartphone. Hence, this error cannot be compensated, e.g. by calibration. In this paper, we for the first time provide a solution to detect the wireless channel on which a packet has been received by a smartphone application. We experimentally evaluate our proposed technique on multiple different smartphone models. Our results help to make distance estimation on smartphones more robust and accurate.

Bluetooth low energy (BLE)-based location service technology has become one of the fastest growing applications for Bluetooth. Received signal strength (RSS) is often used in localization techniques for ranging or location fingerprinting. However, RSS-based localization solutions have poor performance in multipath environments. In this paper, we present a measurement system designed using multiple ESP32 BLE modules and the Bluetooth mesh networking technology, which is capable of exploiting the space, time, and frequency diversities in measurements. To enable channel-aware multi-device RSS measurements, we also designed a communication protocol to associate channel ID information to advertising messages. Based on channel measurement and analysis, we demonstrate that with a six-receiver configuration and space-time-frequency diversity combining, we can significantly reduce the residual linear regression fitting errors in path loss models. Such a reduction leads to more accurately correlating RSS measurements to the distance between the transmitter and receiver devices and thus to achieving improved performance with the RSS-based localization techniques. More importantly, the reduction in the fitting errors is achieved without differentiating the three advertising channels, making it possible to conveniently implement the proposed six-receiver configuration using commercially available BLE devices and the standard Bluetooth mesh networking protocol stack.

Objectives: The objective of this study is to develop a Bluetooth-based low-cost wearable device for a self-quarantine monitoring system. Study design: The designed wearable device focuses on data transmission via Bluetooth, integration of tracking, tracing, and fencing into a single system, and low energy usage from its battery. Methods: We design a wearable device using smartphone equipped with GPS, a communication module, Bluetooth low energy (BLE) and a high-capacity battery as a solution for low-cost device with excellent efficiency. We divide the designed system into two parts, the client and the server parts. The client parts are wearable device attached to the individual being monitored and the mobile phone as GPS and telecommunications module. Whereas the server parts are user interface, digital map, notification system, and backend database. Then, the whole system was tested in laboratory and field scale. Results: We tested functions of integrated device such as wearable device, mobile applications, and server for laboratory scale test. Then, performing field test with geofencing, communication module, battery, web interface, and resource computing usage. The field test was conducted on a small scale with a limited number of trial patients. We found that the designed wearable device was successfully implemented for both self-quarantine and centralized quarantine requirements. The majority of the components used met the specifications and functioned properly as well. Conclusions: A BLE-enabled wearable device can be used for tracking self-quarantine patients. The laboratory and field scale tests demonstrate that the designed wearable device functions properly and meets the requirements. We anticipate that this low-cost wearable device is effective in limiting Covid-19 virus spread and preventing the formation of a new Covid-19 virus-infected cluster.

Lodging risk in spring wheat has grown with the introduction of high-yielding cultivars that require high nitrogen (N) inputs to optimize grain yield and protein content. Canopy management (CM) strategies have been used to reduce lodging risk. However, the short growing season and unpredictable moisture supply may limit the use of these strategies in the Canadian Prairies. The objective of this study was to quantify the ability of agronomic practices to modify the early season crop canopy, and hence lodging risk, and to determine how these practices influence N use and final grain N yield. Split-plot field trials were established at four site-years (SYs) in Manitoba to evaluate CM strategies including three plant densities, a split N fertilizer application, and the application of chlormequat chloride (CCC) on canopy size (dry matter (DM) and height at anthesis), N uptake and remobilization, lodging and stalk strength, grain yield, protein, and grain N yield on the cultivar AAC Brandon. Precipitation was low during this study (64%–78% of long-term average (LTA)), resulting in modest yields and low lodging pressure. However, low plant density (PD), split N, and CCC applications all showed potential to reduce lodging risk without reducing grain yield or protein concentration. Split N and plant growth regulator (PGR) applications increased final grain N yield by 11.2% and 2.4%, respectively. Therefore, CM practices have potential in the Canadian Prairies to reduce lodging risk without compromising grain N, but results should be validated in higher precipitation environments and across additional cultivars and growing regions of western Canada. Les risques de verse chez le blé de printemps se sont accrus avec l'introduction de cultivars à haut rendement dont on optimise le rendement et la teneur en protéines par un apport important d'engrais azoté (N). Pour atténuer ce risque, les producteurs recourent à la gestion du feuillage. Cependant, la brièveté de la période végétative et une teneur en eau imprévisible dans le sol pourraient restreindre le recours à une telle stratégie dans les Prairies canadiennes. Les auteurs voulaient déterminer dans quelle mesure les pratiques agricoles permettraient de modifier la densité du feuillage, donc les risques de verse, en début de saison et établir comment de telles pratiques influent sur l'utilisation du N et la concentration finale de cet élément dans le grain. Pour cela, ils ont procédé à des essais en tiroir sur le terrain pendant quatre années-sites, au Manitoba. Les méthodes de gestion du feuillage examinées comprenaient trois densités de feuillage, l'application fractionnée de N et l'application de chlorure de chlorméquat (CC). Les chercheurs ont évalué l'incidence de ces stratégies sur l'abondance du feuillage (poids sec et hauteur à l'anthèse) du cultivar AAC Brandon, l'absorption et la relocalisation du N, la verse et la longueur de la tige, le rendement grainier, la concentration de protéines et le rendement en N du grain. Comme il n'a guère plu pendant l'étude (64 à 78 % de la moyenne à long terme), le rendement était modeste et la pression engendrée par la verse, relativement faible. Néanmoins, un peuplement de faible densité, l'application fractionnée de N et celle de CC sont autant de mesures susceptibles d'atténuer les risques de verse sans que le rendement grainier ou la teneur en protéines du grain en souffrent. L'application fractionnée de N et celle d'un régulateur de croissance ont respectivement augmenté le rendement en N final du grain de 11,2 et de 2,4 %. Les auteurs en concluent que la gestion du feuillage pourrait réduire les risques de verse dans les Prairies canadiennes sans diminuer la teneur en N du grain, mais ces conclusions devront être confirmées lors de précipitations plus abondantes et avec d'autres cultivars ainsi que dans des zones de culture différentes de l'Ouest canadien. [Traduit par la Rédaction] [ABSTRACT FROM AUTHOR]

Lung fibrosis is a serious human pathology. MiR-146b-5p is down-regulated in idiopathic pulmonary fibrosis, and the Notch1/PDGFRβ/ROCK1 pathway is activated. However, the relation between miR-146b-5p and the Notch1/PDGFRβ/ROCK1 pathway in lung fibrosis remains unclear. To investigate the function of miR-146b-5p in lung fibrosis, an in vivo model of lung fibrosis was established in mice by bleomycin. The fibrosis in lung tissues of mice was observed by HE, Masson and Sirius Red staining. Lung pericytes were isolated and identified by fluorescence microscopy. Immunofluorescence staining and Western blot were used to investigate the expression of desmin, NG2, collagen I and α-SMA. CCK8 assay was used to assess the cell viability, and flow cytometry was performed to evaluate the cell cycle in pericytes. Furthermore, the correlation between miR-146b-5p and Notch1 was analysed by Spearman analysis. The mechanism by which miR-146b-5p affects pericytes and lung fibrosis via the Notch1/ PDGFRβ/ROCK1 pathway was explored by RT-qPCR, Western blot, immunofluorescence staining and dual luciferase reporter gene assay. In bleomycin-treated mice, miR-146b-5p was down-regulated, while Notch1 was up-regulated. Up-regulation of miR-146b-5p significantly inhibited the viability and induced G1 phase arrest of lung pericytes. MiR-146b-5p mimics up-regulated miR-146b-5p, desmin, and NG2 and down-regulated α-SMA and collagen I in the lung pericytes. Additionally, miR-146b-5p was negatively correlated with Notch1, and miR-146b-5p interacted with Notch1. Over-expression of miR-146b-5p inactivated the Notch1/PDGFRβ/ROCK1 pathway. Our results indicate that up-regulation of miR-146b-5p inhibits fibrosis in lung pericytes via modulation of the Notch1/PDGFRβ/ROCK1 pathway. Thus, our study might provide a novel target against lung fibrosis. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) merupakan jaringan nirkabel untuk memantau kondisi fisik atau lingkungan, seperti suhu dan kelembapan, yang kemudian data tersebut diteruskan melalui sebuah jaringan kepada suatu lokasi (komputer) di mana data tersebut dapat diamati dan dianalisis. Tujuan utama WSN adalah menyediakan komunikasi nirkabel berdasarkan jaringan sensor berbiaya rendah dengan konsumsi daya yang sangat terbatas. Sehingga konsumsi energi merupakan permasalahan yang menantang pada WSN. Untuk mengurangi konsumsi energi pada WSN, protokol routing harus diterapkan. Salah satu tantangan pada routing yang terlibat dalam WSN, yaitu lalu lintas data yang dibuat memiliki redundansi (data yang tidak perlu/dibutuhkan) yang signifikan dalam sebagian besar kasus. Selain itu, perangkat WSN bergantung pada baterainya sebagai sumber listrik, sehingga masa pakai jaringan tergantung pada level baterai yang tersisa pada setiap node. Teknologi yang digunakan tiap node untuk saling berkomunikasi juga mempengaruhi konsumsi energi yang ada. Maka pada penelitian ini dirancang sebuah WSN yang berfokus pada pengiriman data dari tiap-tiap sensor node hingga mencapai sink node dengan mekanisme routing berdasarkan daya baterai pada node, sehingga data akan diteruskan melalui node yang memiliki daya terbesar. Protokol komunikasi yang digunakan adalah BLE yang sudah tertanam di ESP32. Metode komunikasi yang digunakan adalah connection-oriented atau master-slave, dengan shared-slave topology (SST) sebagai topologinya. DHT22 digunakan untuk mengukur suhu dan kelembapan relatif. Dari hasil percobaan, DHT22 memiliki error pada pembacaan suhu sebesar 1,935%, sementara pada pembacaan kelembapan udara memiliki error sebesar 1,90%. Hasil algrotima routing yaitu node menentukan dan mengirimkan data suhu dan kelembapan kepada node yang memiliki daya terbesar dengan rata-rata waktu 8,957 detik. Serta rata-rata waktu yang diperlukan oleh node untuk mengirimkan data suhu dan kelembapan kepada sink node yaitu 16,952 detik dengan 3 kali hop.

Bluetooth Low Energy (BLE) is a positioning technology that is commonly used in indoor positioning systems (IPS) such as shopping malls or underground parking lots, because of its low power consumption and the low cost of Bluetooth devices. It also maintains high positioning accuracy. Since the cost of BLE itself is low, it has now been used in larger environments such as parking lots or shopping malls for a long time. However, it is necessary to configure a large number of devices in the environment to obtain accurate positioning results. The most accurate method of using signal strength for positioning is the signal pattern-matching method. The positioning result is compared through a database with the overheads of time and labor costs, since the amount of data will be proportional to the size of the environment for BLE-IPS. A planar model that conforms to the signal strength in the environment was generated, wherein the database comparison method is replaced by an equation solution, to improve various costs but diminish the positioning accuracy. In this paper, we propose to further replace the planar model with a cost-effective fitting model to both save costs and improve positioning accuracy. The experimental results demonstrate that this model can effectively reduce the average positioning error in distance by 31%. [ABSTRACT FROM AUTHOR]

Fusarium graminearum is a mycotoxin-producing pathogen involved in Fusarium head blight (FHB) disease of cereals, and is responsible for compromising grain yield and quality.Over the course of infection, the fungus secretes various metabolites and proteins, of which only a handful have been fully characterized in the context of FHB. We report herein the role of a secreted cerato-platanin protein (FGSG_10212, FgCPP1) in the growth and aggressiveness of F. graminearum. Pathogen strains in which FgCPP1 was disrupted (Δfgcpp1) or overexpressed (FgCPP1-OX1) did not differ from the wild-type with respect to macroconidial germination or mycelial growth, nor in sensitivity to chemicals targeting fungal cell walls. Host—plant interaction studies were conducted including disease assays in wheat and host-cell localization of FgCPP1 in tobacco cells. Consistent with previous reports for FgCPP1 disruption mutants of F. graminearum, no difference was observed among FgCPP1-OX1, Δfgcpp1 and the wild-type in their ability to cause disease spread in point-inoculated wheat spikes. Spray inoculation of the wheat spikes with FgCPP1-OX1 resulted in an increase in diseased spikelets as early as 3 days post-inoculation in each of the three wheat genotypes assessed; however, this increase was not observed when the disease assay was repeated in the cultivar 'Penhold' with an additional FgCPP1-OX transformant, FgCPP1-OX2. FgCPP1-OX1 also resulted in increased levels of deoxynivalenol accumulation in axenic culture, as well as in spray-inoculated wheat spikes. We propose that FgCPP1 is a minor contributor to pathogen aggressiveness in wheat. [ABSTRACT FROM AUTHOR]

The integration of infectious disease modeling with the data collection process is crucial to reach its maximum potential, and remains a significant research challenge. Ensuring a solid empirical foundation for models used to fill gaps in data and knowledge is of paramount importance. Personal wireless devices, such as smartphones, smartwatches and wireless bracelets, can serve as a means of bridging the gap between empirical data and the mathematical modeling of human contacts and networking. In this paper, we develop, implement, and evaluate concepts and architectures for advanced user-centric proximity estimation based on smartphone radio environment monitoring. We investigate innovative methods for the estimation of proximity, based on a person-radio-environment trace recorded by the smartphone, and define the proximity parameter. For this purpose, we developed a smartphone application and back-end services. The results show that, with the proposed procedure, we can estimate the proximity of two devices in terms of near, medium, and far distance with reasonable accuracy in real-world case scenarios. [ABSTRACT FROM AUTHOR]

Obstacle avoidance is a key aspect for any autonomous vehicles, and their usage in agriculture must overcome additional challenges such as handling interactions with agricultural workers and other tractors in order to avoid severe accidents. The simultaneous presence of autonomous vehicles and workers on foot definitely calls for safer designs, vehicle management systems and major developments in personal protective equipment (PPE). To cope with these present and future challenges, the "SMARTGRID" project described in this paper deploys an integrated wireless safety network infrastructure based on the integration of Bluetooth Low Energy (BLE) devices and passive radio frequency identification (RFID) tags designed to identify obstacles, workers, nearby vehicles and check if the right PPE is in use. With the aim of detecting workers at risk by scanning for passive RFID-integrated into PPE in danger areas, transmitting alerts to workers who wear them, tracking of near-misses and activating emergency stops, a deep analysis of the safety requirements of the obstacle detection system is shown in this study. Test programs have also been carried out on an experimental farm with detection ranging from 8 to 12 meters, proving that the system might represent a good solution for collision avoidance between autonomous vehicles and workers on foot. [ABSTRACT FROM AUTHOR]

Beacons are small devices which are playing an important role in the Internet of Things (IoT), connecting “things” without IP connection to the Internet via Bluetooth Low Energy (BLE) communication. In this paper we present the first private end-to-end encryption protocol called the Eddystone-Ephemeral-ID (Eddystone-EID) protocol. This protocol enables connectivity from any beacon to its remote owner, while supporting beacon’s privacy and security, and essentially preserving the beacon’s low power consumption. We describe the Eddystone-EID development goals, discuss the design decisions, show the cryptographic solution, and analyse its privacy, security, and performance. Finally, we present three secure IoT applications built on Eddystone-EID, demonstrating its utility as a security and privacy infrastructure in the IoT domain. Further, Eddystone-EID is a prototypical example of security design for an asymmetric system in which on one side there are small power-deficient elements (the beacons) and on the other side there is a powerful computing engine (a cloud). The crux of the design strategy is based on: (1) transferring work from the beacon to the cloud, and then (2) building a trade-off between cloud online work against cloud offline work, in order to enable fast real-time reaction of the cloud. These two principles seem to be generic and can be used for other problems in the IoT domain. [ABSTRACT FROM AUTHOR]

In order to achieve the full potential of the Internet-of-Things, connectivity between devices should be ubiquitous and efficient. Wireless mesh networks are a critical component to achieve this ubiquitous connectivity for a wide range of services, and are composed of terminal devices (i.e., nodes), such as sensors of various types, and wall powered gateway devices, which provide further internet connectivity (e.g., via Wi-Fi). When considering large indoor areas, such as hospitals or industrial scenarios, the mesh must cover a large area, which introduces concerns regarding range and the number of gateways needed and respective wall cabling infrastructure, including data and power. Solutions for mesh networks implemented over different wireless protocols exist, like the recent Bluetooth Low Energy (BLE) 5.1. While BLE provides lower power consumption, some wall-power infrastructure may still be required. Alternatively, if some nodes are battery powered, concerns such as lifetime and packet delivery are introduced. We evaluate a scenario where the intermediate nodes of the mesh are battery powered, using a BLE relay of our own design, which acts as a range extender by forwarding packets from end-nodes to gateways. We present the relay’s design and experimentally determine the packet forwarding efficiency for several scenarios and configurations. In the best case, up to 35% of the packets transmitted by 11 end-nodes can be forwarded to a gateway by a single relay under continuous operation. A battery lifetime of 1 year can be achieved with a relay duty cycle of 20%. [ABSTRACT FROM AUTHOR]

The article presents the results of experimental research related to the optimization of the placement of BLE transmitters (beacons) inside rooms in order to locate the user. The experiments were carried out to check how far BLE transmitters can be moved away from each other so that the average localization error was at a level acceptable to the user. The value of the localization error was assumed based on the literature (less than 1 m). As a result of the conducted experiments, a mathematical formula was defined to calculate the required number of BLE transmitters for the exact location inside the rooms. The experiments used BLE transmitters, rooms inside the building of Siedlce University of Natural Sciences and Humanities and a mobile application with a database. In the study of the optimization of the placement of beacons, the fingerprint technique was used. [ABSTRACT FROM AUTHOR]

The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Indoor positioning approaches apply different types of sensors to increase the accuracy of the user's position. Among these technologies, Bluetooth Low Energy (BLE) appeared as a popular alternative due to its low cost and energy efficiency. However, BLE faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This work presents a method to compensate RSSI values by applying Artificial Neural Network (ANN) algorithms to RSSI measurements from three BLE advertising channels and a wearable camera as an additional source of information for the presence or absence of human obstacles. The resulting improved RSSI values are then converted into ranges using path loss models, and trilateration is applied to obtain indoor localization. The proposed artificial system provides significantly better localization solutions than fingerprinting or trilateration using uncorrected RSSI values. [ABSTRACT FROM AUTHOR]