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In Wireless Sensor Networks which are deployed in remote and isolated tropical areas; such as forest; jungle; and open dirt road environments; wireless communications usually suffer heavily because of the environmental effects on vegetation; terrain; low antenna height; and distance. Therefore; to solve this problem; the Wireless Sensor Network communication links must be designed for their best performance using the suitable electromagnetic wave behavior model in a given environment. This study introduces and analyzes the behavior of the LoRa pathloss propagation model for signals that propagate at near ground or that have low transmitter and receiver antenna heights from the ground (less than 30 cm antenna height). Using RMSE and MAE statistical analysis tools; we validate the developed model results. The developed Fuzzy ANFIS model achieves the lowest RMSE score of 0.88 at 433 MHz and the lowest MAE score of 1.61 at 433 MHz for both open dirt road environments. The Optimized FITU-R Near Ground model achieved the lowest RMSE score of 4.08 at 868 MHz for the forest environment and lowest MAE score of 14.84 at 868 MHz for the open dirt road environment. The Okumura-Hata model achieved the lowest RMSE score of 6.32 at 868 MHz and the lowest MAE score of 26.12 at 868 MHz for both forest environments. Finally; the ITU-R Maximum Attenuation Free Space model achieved the lowest RMSE score of 9.58 at 868 MHz for the forest environment and the lowest MAE score of 38.48 at 868 MHz for the jungle environment. These values indicate that the proposed Fuzzy ANFIS pathloss model has the best performance in near ground propagation for all environments compared to other benchmark models

In this paper a modified mathematical model based on the SIR model used which can predict the spreading of the corona virus disease (COVID-19) and its effects on people in the days ahead. This model takes into account all the death, infected and recovered characteristics of this disease. To determine the extent of the risk posed by this novel coronavirus; the transmission rate (R0) is utilized for a time period from the beginning of spreading virus. In particular, it includes a novel policy to capture the R0 response in the virus spreading over time. The model estimates the vulnerability of the pandemic with a prediction of new cases by estimating a time-varying R0 to capture changes in the behavior of SIR model implies to new policy taken at different times and different locations of the world. This modified SIR model with the different values of R0 can be applied to different country scenario using the real time data report provided by the authorities during this pandemic. The effective evaluation of R0 can forecast the necessity of lockdown as well as reopening the economy.

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Palm oil is the main cash crop of tropical Asia, and the implementation of LPWAN (low-power wide-area network) technologies for smart agriculture applications in palm oil plantations will benefit the palm oil industry in terms of making more revenue. This research attempts to characterize the LoRa 433 MHz frequency channels for the available spreading factors (SF7-SF12) and bandwidths (125 kHz, 250 kHz, and 500 kHz) for wireless sensor networks. The LoRa channel modeling in terms of path-loss calculation uses empirical measurements of RSS (received signal strength) in a palm oil plantation located in Selangor, Malaysia. In this research, about 1500 LoS (line-of-sight) and 300 NLoS (non-line-of-sight) propagation measurement data are collected for path-loss prediction modeling. Using the empirical data, a prediction model is constructed. The path-loss exponent for LoS propagation of the proposed prediction model is found to be 2.34 and 2.9 for 125–250 kHz bandwidth and 500 kHz bandwidth, respectively. Again, for the NLoS propagation links, the attenuation per trunk is found to be 7.58 dB, 7.04 dB, 5.35 dB, 5.02 dB, 5.01 dB, and 5 dB for SF7-SF12, and the attenuation per canopy is found to be 9.32 dB, 7.96 dB, 6.2 dB, 5.89 dB, 5.79 dB, and 5.45 dB for SF7-SF12. Moreover, the prediction model is found to be the better choice (mean RMSE 2.74 dB) in comparison to the empirical foliage loss models (Weissberger’s and ITU-R) to predict the path loss in palm oil plantations.

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Due to a great number of composition-processing factors, it is very difficult to design high entropy amorphous alloys without performing manifold trial-and-error experimentations. To solve this problem, in this study we developed a machine learning-based approach, namely multilateral-based neural network, which is able to predict new high entropy amorphous compositions through estimating the highest glass forming ability and the critical casting thickness. In this approach, the entropy parameters were individually correlated to each input, which leads to the improvement of predictive model in evaluating the high entropy glassy alloys. As a case study, Ti20Zr20Hf20Be20Co20 high entropy metallic glass (MG) was considered and the effects of added elements such as Y, Ni, Cr and V and Cu on the glass formation and critical casting thickness were investigated. According to the results, it is determined that the Y addition acts as a microalloying process in the base composition, while other elements improve the configurational entropy and the total negative heat of mixing, which lead to the engineering of equi-atomic high entropy MGs.

Palm oil is a vital cash crop in tropical Asia. Implementation of technology in palm oil industry not only increase production but also it reduces the plantation management cost significantly. Affordability of smart devices of the farm owners is the major concern behind not using technologies in production and process. There are a lot of technologies are being used in agriculture sectors to ease the manual labour with reduced cost. Study on the challenges faced by the plantation owners is needed to introduce new technological solutions for the plantation management in Malaysia. This article reviews some problems on technology-based monitoring. A qualitative research has been conducted to address the problems faced by the authority of Palm oil plantation. Based on the problem statement researcher introduced a conceptual study of LoRaWAN (Low power wide area network) embedded system suitable for low cost energy efficient Palm oil plantation monitoring. Installation of IoT based device will ensure smart monitoring as well as handling the agriculture activities of the Palm oil plantation with reduced manpower resources.