IoT-based sound-level control for audio amplifiers: mosques as a case study

When using audio-amplifiers in the open, uneven distribution of sound makes people unpleasant because it is loud or unheared. This unfortunate situation arises because audio-amplifiers volumes are set according to the guess of sound technicians. Mosques, as an example, are distributed inside wide areas and fire Azan five times a day. Due to the relatively long distances between them, speed and direction of the wind impose setting sound levels prior to each Azan such that all the area is covered and the overlap is minimized. In this paper, we propose a system based on internet of things (IoT) model to control the sound level of each mosque in the community. An IoT device (one in a mosque) sets the level of sound fired by the audio-amplifier. To do that, a synchronized series of tones is fired from each node. Once a node hears these tones, the process of sound level control starts to indicate the distances to heared nodes. As the approximate distances between nodes are known, each node can calculate its suitable sound level. Results showed that the proposed system is effective in setting sound levels for mosques audio amplifiers

QoS and security-aware task assignment and scheduling in real-time systems

Security issues in mission-critical real-time systems (e.g., command and control systems) are becoming increasingly important as there are growing needs for satisfying information assurance in these systems. In such systems, it is important to guarantee real-time deadlines along with the security requirements (e.g., confidentiality, integrity, and availability) of the applications. Traditionally, resource management in real-time systems has focused on meeting deadlines along with satisfying fault-tolerance and/or resource constraints. Such an approach is inadequate to accommodate security requirements into resource management algorithms. Based on the imprecise computation paradigm, a task can have several Quality of Service (QoS) levels, higher QoS result incurs higher computational cost. Similarly, achieving a higher level of confidentially requires stronger encryption, which incurs higher computational cost. Therefore, there exists a tradeoff between schedulability of the tasks on the one hand, and the accuracy (QoS) and security of the results produced on the other hand. This tradeoff must be carefully accounted in the resource management algorithms. In this context, this dissertation makes the following contributions: (i) formulation of scheduling problems accounting both deadline and security requirements of workloads in real-time systems, (ii) development of novel task allocation and scheduling algorithms for such workloads, (iii) and evaluation of the results through simulation studies and a limited test evaluations in one case. In particular, the following are the three key contributions. Firstly, the problem of scheduling a set of non-preemptable real-time tasks with security and QoS requirements with the goal of maximizing integrated QoS and security of the system is addressed. This problem is formulated as MILP, and then its complexity is proved to be NP-hard. An online efficient heuristic algorithm is developed as the problem is NP-hard. Simulation studies for a wide range of workload scenarios showed that the proposed algorithm outperforms a set of baseline algorithms. Further, the proposed algorithm\u27s performance is close to the optimal solution in a specific special case of the problem. Secondly, a static assignment and scheduling of a set of dependent real-time tasks, modeled as Directed Acyclic Graph (DAG), with security and QoS requirements in heterogeneous real-time system with the objective of maximizing Total Quality Value (TQV) of the system is studied. This problem is formulated as MINLP. Since this problem is NP-hard, a heuristic algorithm to maximize TQV while satisfying the security constraint of the system is developed. The proposed algorithm was evaluated through extensive simulation studies and compared to a set of baseline algorithms for variations of synthetic workloads. The proposed algorithm outperforms the baseline algorithms in all the simulated conditions for fully-connected and shared bus network topologies. Finally, the problem of dynamic assignment and scheduling of a set of dependent tasks with QoS and security requirements in heterogeneous distributed system to maximize the system TQV is addressed. Two heuristic algorithms to maximize TQV of the system are proposed because the problem is NP-hard. The proposed algorithms were evaluated by extensive simulation studies and by a test experiment in InfoSpher platform. The proposed algorithms outperform the baseline algorithms in most of the simulated conditions for fully-connected and shared bus network topologies

One-step hydroxylation of benzene to phenol using N2O

There is an increasing commercial interest in finding alternative ways to produce phenol that overcome the disadvantages of the current cumene process used to synthesize phenol. The drivers for the change are both economic and environmental. A direct oxidation route for producing phenol from benzene is based on using N2O as an oxidizing agent in the gas phase in the presence of modified Fe-ZSM5 zeolite. One of the main objectives was to examine the effect of different Si/Al ratios, temperatures and iron content on the selective conversion of benzene to phenol with a desire to achieve high selectivity and minimise catalyst deactivation. Also one of the research objectives was to identify the active sites in the catalyst and design the catalyst which is able to delay coke formation. The methodology was to incorporate iron directly at extra-framework positions via liquid ion-exchange. In this project, a series of selective Fe-ZSM5 catalysts with different Si/Al ratios have been prepared and evaluated for selective formation of phenol. The catalyst samples were characterized (by Atomic Absorption Spectroscopy (AAS), Malvern mastersizer and Nitrogen adsorption using N2 at 77 K via Micromeritics to determine the elemental composition, average particle size, BET surface area and pore size distribution) and their catalytic activities compared. A quantitative comparison between the number of active sites using isopropylamine decomposition method shows that active sites increase as the Si/Al ratio increased and also as the iron content increased. (Continues...)

A Unified Call-to-Prayer Framework in Muslim World

In many Muslim countries there are many sounds that are fired at nearly the same time via loudspeakers. This sound is a call-to-prayer (Azan). Azan is fired from the so-called mosques in many countries where, theses mosques are still using its own timing to trigger such call and its own amplifier gain regardless of other mosques in the region. This results in an out of sync call-to-prayer firing and a very noisy and distracting mix of sounds in many places at the same region. In this paper, a unified call-to-prayer framework is proposed that sheds light on these issues and gives solution directions for the above mentioned issues in the currently used systems.DOI:http://dx.doi.org/10.11591/ijece.v4i3.575

ENVIRONMENTAL HEALTH RISKS TO FARMERS AS A RESULT OF PESTICIDES’ MISMANAGEMENT IN KHANYOUNIS GOVERNORATE, GAZA STRIP

Several poisoning and death cases were reported in Khanyounis Governorate as a result of mis-use and mishandling of pesticides. Carcinogenic and internationally banned pesticides are still available in the markets in all the governorates of the Gaza Strip. This study aims to investigate the awareness and health issues of the farmers. Fortyfive farmers were randomly selected to fill a questionnaire prepared for this purpose. The results showed that protective clothing are totally not worn during application of pesticides. Lack of storage faculties, unlicensed pesticide shops, improper disposal of the empty containers and smoking and eating during application are among the hot spots related to pesticides handling and application. Also, 44.5% of the farmers complain of health problems, of which headache, coughing, skin rashes and difficulty in breathing. Only 4.5% of the farmers attended general agricultural training courses. In conclusion the study suggests that the government, public, the non-governmental organizations and all the interested parties should cooperate in a collective and serious work to minimize these environmental and health risks

Dokaz protutijela za kugu malih preživača i goveđu kugu u saudijskih ovaca i koza u prirodnim uvjetima.

This study represents the first survey for serum antibodies against peste des petits ruminants (PPR) and rinderpest (RP) viruses, in sheep and goats in the Kingdom of Saudi Arabia. The study involved the Eastern region of the country. A total of 1035 serum samples were examined (750 sheep and 285 goats). In order to obtain a genuine insight into the activity of the two viruses as reflected by seroconversion, serum samples were collected only from sedentary, locally-bred, non-vaccinated sheep and goats that were more than one year old. The number of samples collected followed standard epidemiological criteria in similar situations. The prevalence of PPR virus antibodies was 3.1% in sheep and 0.6% in goats, while that of RPV antibodies was 3.6% in sheep and 5.7% in goats. Generally speaking, the prevalence of PPRV antibodies in both species was 2.3%, while that of RPV was 4.3%.The mono-specific reactivity in both species was 93.2 % for rinderpest and 66.7 % for PPR.Rad iznosi prve rezultate istraživanja protutijela za virus kuge malih preživača (KMP) i virus goveđe kuge (GK) u ovaca i koza u Saudijskoj Arabiji. Istraživanjem je bilo obuhvaćeno istočno područje zemlje. Pretraženo je ukupno 1035 uzoraka seruma (750 ovčjih i 285 kozjih). Radi dobivanja stvarne slike aktivnosti ovih dvaju virusa, uzorci seruma bili su sakupljani samo od domaćih, lokalno rasplođivanih, nevakciniranih ovaca i koza starijih od godinu dana. Broj sakupljenih uzoraka bio je u skladu sa standardnim epidemiološkim kriterijima. Proširenost protutijela za virus KMP iznosila je 3,1% u ovaca i 0,6% u koza, dok je proširenost protutijela za virus GK bila 3,6% u ovaca i 5,7% u koza. Općenito prikazana, proširenost KMP u obje vrste iznosila je 2,3%, dok je za GK iznosila 4,3%. Monospecifična reaktivnost u obje vrste bila je 93,2% za GK i 66,7% za KMP

Forecasting Energy Consumption in Smart Grids: A Comparative Analysis of Recurrent Neural Networks

In the present era of smart grids, accurate prediction of energy uses is becoming increasingly essential to guarantee optimal energy efficiency. This study contributes to the field by utilizing advanced machine learning techniques to perform predictions of energy consumption using the data from Internet of Things (IoT) devices. Specifically, the approach utilizes regression neural network (RNN) structures, such as long short-term memory (LSTM) and gated recurrent units (GRUs). The data from IoT sensors are more extensive and detailed than those of conventional smart meters, allowing for the development of more complex models of energy use patterns. This study utilizes Adam-optimized LSTM, RNN, and GRU models, along with stochastic gradient descent, to evaluate their performance in addressing the complexity of time-series data in energy forecasting on different network configurations. Result of the analysis indicates that LSTM models, which are run with the Adam optimizer, are more accurate in terms of predictions compared with the other models. This conclusion is supported by the test results of these models that are within the lowest root mean square error and mean absolute error scores. All the models under the analysis exhibit signs of overfitting based on the performance indicators for the training and the testing data. This notion implies that regularization should be utilized to ensure the improved generalizability of the models. These findings show that deep learning can have a lasting influence in improving energy consumption management systems to meet sustainability and energy efficiency requirements. These observations are beneficial for the gradual improvements of smart grids

Bayesian estimation for the Tukey GH distribution with an application

Tukey GH is a transformed normal distribution, having two parameters (g,h). The g parameter represents skew measuring, and the h represents kurtosis measuring. Our motivation is to affect these parameters on the behavior for a simulation data and real data, such as the Iraqi stock market Index (ISX60) and the Standard and Poor’s (SP500) index using the Bayesian framework. Then, our aim is to find the estimation for the parameters in this distribution using empirical and parametric Bayesian methods, and study the effective on the simulation and real data. The simulation study will be shown the behavior of the parameters with a different number of sampling and prior distributions. We will use the real data from ISX60 and SP500 index

Cancer patients with Angiotensin-converting enzyme (ACE) gene polymorphism and COVID-19 phenotypic expression predisposed to SARS-CoV-2 infection

Pathogenesis of COVID-19 has been linked to the Angiotensin system. Angiotensin-converting enzyme (ACE2) has been recognized as the specific receptor of the SARS-CoV-2 virus, serves as a cellular receptor for SARS-CoV-2, suggesting that a person's vulnerability to infection may be controlled by how much of the ACE2 gene is expressed. It is also possible that the severity of COVID-19 is related to the equilibrium between ACE1 and ACE2 activity, which has been linked to the etiology of respiratory disorders. This study aimed to investigate the association of ACE1 I/D polymorphism with the severity of Covid-19. The study looked at 113 people-(50 healthy controls, 63 people with Covid). Results for the ACE2 rs4240157 T > C polymorphism were obtained. Logistic regression was used to evaluate the distribution frequencies of variables across the study groups. The ACE1-CC*CT genotype (p = 0.049) and male gender (p0.001) were related to severe COVID-19. COVID-19 severity was found to be associated with the ACE2–CT genotype through multiple logistic regression under the co-dominant inheritance model: CC*CT Allele, 95% CI (0.0104 to 0.2954), Significance level, (0.0007) Odd Ratio (0.0556); CC*TT Allele, 95% CI (0.1854 to 6.1927), Significance level, (0.9386) Odd Ratio (1.0714); and CT*TT (19.2857). This was assuming the ACE2–CC*CT genotype was connected with covid-19 severity. However, the ACE2 polymorphism did not affect the development of illness. In conclusion, male gender, malignancy, and the ACE1 genotype were linked to a negative result of COVID-19. Our results indicated that ACE1-C/T might affect COVID-19 severity; however, this association was hypertensive status-specific. However, this finding needs to be confirmed in additional large samples