RESUMEN
Robust wireless communication using relaying system and Non-Orthogonal Multiple Access (NOMA) will be extensively used for future IoT applications. In this paper, we consider a fall detection IoT application in which elderly patients are equipped with wearable motion sensors. Patient motion data is sent to fog data servers via a NOMA-based relaying system, thereby improving the communication reliability. We analyze the average signal-to-interference-plus-noise (SINR) performance of the NOMA-based relaying system, where the source node transmits two different symbols to the relay and destination node by employing superposition coding over Rayleigh fading channels. In the amplify-and-forward (AF) based relaying, the relay re-transmits the received signal after amplification, whereas, in the decode-and-forward (DF) based relaying, the relay only re-transmits the symbol having lower NOMA power coefficient. We derive closed-form average SINR expressions for AF and DF relaying systems using NOMA. The average SINR expressions for AF and DF relaying systems are derived in terms of computationally efficient functions, namely Tricomi confluent hypergeometric and Meijer's G functions. Through simulations, it is shown that the average SINR values computed using the derived analytical expressions are in excellent agreement with the simulation-based average SINR results.
RESUMEN
Worldwide, acute antipsychotic poisoning results in high morbidities and mortalities. Though extrapyramidal syndromes are commonly associated, the extent of extrapyramidal syndromes in relation to the severity of antipsychotic poisoning has not been addressed yet. Thus, this study aimed to assess the Global Dystonia Rating Scale (GDRS) as an unfavorable outcomes predictive tool in acute antipsychotic poisoning. A cross-sectional study included 506 antipsychotic-poisoned patients admitted to Tanta University Poison Control Center, Egypt, over three years was conducted. The mean GDRS was 9.1 ± 16.7 in typical antipsychotic poisoning, which was significantly higher than that of atypical antipsychotics (4.2 ± 11.5) (p = 0.003). Patients with GDRS< 20 showed significantly higher liability for all adverse outcomes (p < 0.05). However, poisoning with typical antipsychotics was associated with significantly more cardiotoxicity (p = 0.042), particularly prolonged QRS (p = 0.005), and intensive care unit (ICU) admission (p = 0.000). In contrary to the PSS, which failed to predict the studied adverse outcomes, GDRS significantly predicted all adverse outcomes (p < 0.000) for all antipsychotic generations. In atypical antipsychotics, GDRS above three accurately predicted cardiotoxicities, prolonged QTc interval, and respiratory failure with Area under curves (AUC) of 0.937, 0.963, and 0.941, respectively. In typical antipsychotic poisoning, at higher cutoffs (7.5, 27.5, 18, and 7.5), cardiotoxicities, prolonged QTc interval, and respiratory failure were accurately predicted (AUC were 0.974, 0.961, and 0.960, respectively). GDRS is an objective, substantially useful tool that quantifies dystonia and can be used as an early reliable predictor of potential toxicity in acute antipsychotic poisoning.
RESUMEN
Nowadays, Unmanned Aerial Vehicle (UAV) devices and their services and applications are gaining popularity and attracting considerable attention in different fields of our daily life. Nevertheless, most of these applications and services require more powerful computational resources and energy, and their limited battery capacity and processing power make it difficult to run them on a single device. Edge-Cloud Computing (ECC) is emerging as a new paradigm to cope with the challenges of these applications, which moves computing resources to the edge of the network and remote cloud, thereby alleviating the overhead through task offloading. Even though ECC offers substantial benefits for these devices, the limited bandwidth condition in the case of simultaneous offloading via the same channel with increasing data transmission of these applications has not been adequately addressed. Moreover, protecting the data through transmission remains a significant concern that still needs to be addressed. Therefore, in this paper, to bypass the limited bandwidth and address the potential security threats challenge, a new compression, security, and energy-aware task offloading framework is proposed for the ECC system environment. Specifically, we first introduce an efficient layer of compression to smartly reduce the transmission data over the channel. In addition, to address the security issue, a new layer of security based on an Advanced Encryption Standard (AES) cryptographic technique is presented to protect offloaded and sensitive data from different vulnerabilities. Subsequently, task offloading, data compression, and security are jointly formulated as a mixed integer problem whose objective is to reduce the overall energy of the system under latency constraints. Finally, simulation results reveal that our model is scalable and can cause a significant reduction in energy consumption (i.e., 19%, 18%, 21%, 14.5%, 13.1% and 12%) with respect to other benchmarks (i.e., local, edge, cloud and further benchmark models).
