RM-ADR: Resource Management Adaptive Data Rate for Mobile Application in LoRaWAN

LoRaWAN is renowned and a mostly supported technology for the Internet of Things, using an energy-efficient Adaptive Data Rate (ADR) to allocate resources (e.g., Spreading Factor (SF)) and Transmit Power (TP) to a large number of End Devices (EDs). When these EDs are mobile, the fixed SF allocation is not efficient owing to the sudden changes caused in the link conditions between the ED and the gateway. As a result of this situation, significant packet loss occurs, increasing the retransmissions from EDs. Therefore, we propose a Resource Management ADR (RM-ADR) at both ED and Network Sides (NS) by considering the packet transmission information and received power to address this issue. Through simulation results, RM-ADR showed improved performance compared to the state-of-the-art ADR techniques. The findings indicate a faster convergence time by minimizing packet loss ratio and retransmission in a mobile LoRaWAN network environment.

Modeling of Occupancy-Based Energy Consumption in a Campus Building Using Embedded Devices and IoT Technology

Since occupancy affects energy consumption, it is common to model and simulate occupancy using simulation software. One drawback of simulation software is that it cannot provide data transmission information from the sensors, which is essential for real-time energy monitoring systems. This paper proposes an approach to integrating an occupancy model and a real-time monitoring system for real-time modeling. The integration was performed by implementing a model on embedded devices and employing an IoT-based real-time monitoring application. The experimental results showed that the proposed approach effectively configured and monitored the model using a smartphone. Moreover, the data generated by the model were stored in an IoT cloud server for monitoring and further analysis. The evaluation result showed that the model ran perfectly in real-time embedded devices. The assessment of the IoT data transmission performances yielded a maximum latency of 9.0348 s, jitter of 0.9829 s, inter-arrival time of 5.5085 s, and packet loss of 10.8%, which are adequate for real-time modeling of occupancy-based energy consumption.

Meningococcal Carriage among Household Contacts of Patients with Invasive Meningococcal Disease in Kathmandu, Nepal: A Longitudinal Study

Because asymptomatic carriers are key source of transmission, information on meningococcal carriage in the community provides a scientific basis for appropriate preventive/control strategies. This longitudinal study (January 2017–December 2019) aimed to estimate carriage rate of meningococci among household contacts of meningococcal meningitis cases within Kathmandu Valley, Nepal. Throat swab samples were collected at first visit from each person in households, twice a month for up to 2 months and subsequently on a monthly basis for a further 4 months. Altogether, 1125 throat samples were processed by conventional culture for the identification of meningococci. To the best of our knowledge, this is the first longitudinal study on meningococcal carriage in Nepal. The meningococcal carriage rate among household contacts was 15%. All carriers were aged 19 years or older. There was no statistically significant gender difference. The duration of carriage was 60 days. Twenty of 36 isolates belonged to serogroup A, and 16 were non-serogroupable (NG). Serogroups isolated from the same individuals did not change within the follow-up period. All meningococcal isolates over the past 38 years in Nepal that have been reported in previous studies have belonged to serogroup A. The detection of NG meningococcal isolates in apparently healthy household contacts clearly indicates the importance of vigilance through surveillance and periodic in-depth studies.

Pine wilt disease spreading prevention system using semantic segmentation

Pine wilt disease is a disease that affects ecosystems by rapidly killing trees in a short period of time due to the close interaction between three factors such as trees, mediates, and pathogens. There is no 100% mortality infectious forest pests. According to the Korea Forest Service survey, as of April 2019, the damage of pine re-nematode disease was about 490,000 dead trees in 117 cities, counties and wards across the country. It's a fatal condition. In order to prevent this problem, this paper proposes a system that detects dead trees, early infection trees, and the like, using deep learning-based semantic segmentation. In addition, drones were used to photograph the area of the forest, and a separate pixel segmentation label could be used to identify three levels of transmission information: Suspicion, attention, and confirmation. This allows the user to grasp information such as area, location, and alarm to prevent the spread of re-nematode disease.

Development of Smart Cities with Fog Computing and Internet of Things

In construction of smart cities, Internet of Things and Fog computing have a crucial role to play which requires the need for management and exchange of large amount of information. Both Internet of Things as well as Fog computing are two predominant fields that have emerged in recent years to enable the development of transportation, tourism, industries as well as business in a proficient manner. Hence the introduction of a smart city will require proper study as well as ways to improve the strength’s of the city using technological advancement. This will also enhance the strength of city in many fronts. In this paper, we have examined the positive aspects of fog computing using an IoT architecture that is integrated with fog computing in order to address the issues of network scalability and big data processing. Accordingly, the architecture of the IoT system is built such that the smart city will be able to function in a more efficient manner by means of network transmission, information processing and intelligent perceptions.

FROM THE HISTORY OF RURAL COMMUNITIES OF MOUNTAINOUS DAGHESTAN: TSILITL VILLAGE OF THE GUMBETOVSKY DISTRICT

The article is devoted to an attempt to form a methodology and tools for reconstructing the processes of formation of rural communities in Mountainous Daghestan. This study was carried out on the example of Tsilitl’ settlement of the Gumbetovsky district of Daghestan. The process of formation of the settlement has been traced with the use of archaeological research materials, supplemented by oral traditional ways of transmission information as well as from epigraphic and written sources. Also studied has been its historical development until the beginning of the twentieth century. Based on genealogical schemes and oral traditions, the migration processes due to which migrants from various settlements of Mountainous Daghestan used to settle in Tsilitl’ have been described. These processes are shown on the basis of the tukhum structure of a certain rural community and the origin of each patronimia. Much attention is paid to the geographical description of Tsilitl’. The article presents archive documents of the 19th century, testifying that this community owns vast territories on the northern slope of the Andi range, which are now part of the territory of the Nozhayurtovsky region of the Chechen Republic.

Chronic stress and brain-derived neurotrophic factor

The article discusses the influence of stress on the development of nervous tissue, in particular, on the synthesis of neurotrophins (by the example of the brain-derived neurotrophic factor (BDNF), as the most studied class representative). The biological functions of BDNF are discussed as well as its influence on neuroplasticity and the mechanisms by which the protection of neurons is carried out. The article covers the relationship of the stress-implementing system (hypothalamic-pituitary-adrenal axis) and its main active agent (cortisol) with the BDNF synthesis system at its various levels: from the inhibition of mRNA formation to the mechanisms of postsynaptic signal transmission. Information is also provided on changes of BDNF levels due to the maternal deprivation. Epigenetic changes under the influence of glucocorticoids are also reported. However, it is not only glucocorticoids that alter the functioning of the neurotrophin system. The article provides examples of the reverse effect, enabling to consider neurotrophins as a substance with an anti-stress function. In conclusion, the authors give examples of activities that, according to research, can stimulate the synthesis of neurotrophic factor in the brain.

Encryption technology of voice transmission in mobile network based on 3DES-ECC algorithm

The traditional design of voice collector has poor anti attack ability, which makes the encryption effect of voice transmission poor. Therefore, taking the mobile network voice collector as the research object, 3des-ecc algorithm is applied to the information transmission encryption of the mobile network voice collector.An improved speech signal collector is designed, which combines 3DES and ECC algorithm to realize the encryption of speech transmission information. An improved voice signal collector is designed, which combines 3DES and ECC algorithm to realize the encryption of voice transmission information. In the process of encryption, 168-bit random key is generated first, and it is grouped according to 56 bits as 3DES key, and then the plaintext is encrypted by the key to generate ciphertext; the random key is encrypted by ECC public key of the receiver. The experimental results show that the encryption time of this method is less than 1 s, the data integrity is 93%, and the data loss rate is only 0.33%. It has better anti attack ability, fast encryption speed and good encryption effect.

Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels

(1) Background: In nervous system information is conveyed by a sequence of action potentials, called spikes-trains. As MacKay and McCulloch proposed, spike-trains can be represented as bits sequences coming from Information Sources. Previously, we studied relations between Information Transmission Rates (ITR) carried out by the spikes, their correlations, and frequencies. Here, we concentrate on the problem of how spikes fluctuations affect ITR. (2) Methods: The Information Theory Method developed by Shannon is applied. Information Sources are modeled as stationary stochastic processes. We assume such sources as two states Markov processes. As a spike-trains' fluctuation measure, we consider the Standard Deviation sigma, which, in fact, measures average fluctuation of spikes around the average spike frequency. (3) Results: We found that character of ITR and signal fluctuations relation strongly depends on the parameter s which is a sum of transitions probabilities from no spike state to spike state and vice versa. It turned out that for smaller s (s<1) the quotient ITR\sigma has a maximum and can tend to zero depending on transition probabilities. While for s large enough (1<s) the ITR\sigma is separated from 0 for each s. Similar behavior was observed also when we replaced Shannon entropy terms in Markov entropy formula by their approximation with polynomials. We also show that the ITR quotient by Variance behaves in a completely different way. (4) Conclusions: Our results show that for large transition parameter s the Information Transmission Rate by sigma will never decrease to zero. Specifically, for 1<s<1.7 the ITR will be always, independently on transition probabilities which form this s, above the level of fluctuations, i.e. in this case we have sigma<ITR. Thus, we conclude that in a more noisy environment, to get appropriate reliability and efficiency of transmission, Information Sources with higher tendency of transition from the state no spike to spike state and vice versa should be applied.

Robust association between short-term ambient PM2.5 exposure and COVID prevalence in India

Novel coronavirus (COVID) outbreak is the deadliest pandemic in our lifetime. The COVID prevalence risk may be enhanced due to comorbidity from other health risk factors like air pollution. However, such evidence is still lacking in India. Using daily confirmed cases, ambient PM2.5 (fine particulate matter) exposure and meteorological parameters from 28 major states of India between March 14-June 9, 2020, in a generalized additive model, we estimate the association between short-term PM2.5 exposure and daily COVID confirmed cases. We find that a 10 mg m-3 increase in ambient PM2.5 exposure (with a lag of 0-14 days) is significantly associated with an increased COVID incidence [relative risk (RR) of 1.135 (95% uncertainty interval: 1.091-1.180)] after adjusting for the meteorological factors. A non-linear association between PM2.5 (lag 0-14) and COVID infection predicts an RR of 4.482 (3.357-5.983) for exposure at 60 mg m-3 relative to 25 mg m-3. Our results indicate a significant positive association between ambient PM2.5 exposure and COVID prevalence in India. As India is easing lockdown measures, higher outdoor air pollution may have implications on COVID transmission, information which can be helpful for general public and policymakers alike.