FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods

Antimicrobial properties of Hyssopus officinalis extract against antibiotic-resistant bacteria in planktonic and biofilm form

Introduction: Extensive use of antibiotics led to the development of bacterial resistant to antibiotics. Medicinal plants can be alternative choice for antibiotics. The plant (Hyssopus officinalis) belongs to Lamiaceae family recently were attracted as a source for antimicrobial agents. The aim of this study was to evaluate the antibacterial and inhibitory activity of H. officinalis extract on the growth of six antibiotic-resistant bacteria. Materials and methods: In this study, ethanolic and methanolic extracts of H. officinalis were prepared. Antibacterial activity of the ethanolic and methanolic extracts was evaluated by paper disc diffusion method. Also, MIC and MBC of these extracts were determined for six pathogenic bacteria. The effect of these extracts on biofilm of bacteria (biofilm formation and destruction) was evaluated by microtiter plate method. The chemical composition of the extract was identified by GC-MS. Results: The results of study showed the maximum inhibitory effect of these extracts against planktonic forms belong to Staphylococcus aureus and Pseudomonas aeruginosa. Between all studied bacteria, Acinetobacter baumannii showed the greatest sensitivity to H. officinalis extracts in Muller Hinton broth (MIC= 3.125 mg ml-1). The highest inhibitory effects of H. officinalis ethanolic extract on biofilm formation were observed against Escherichia coli (95 %). The results of biofilm destruction showed that Klebsiella pneumoniae biofilm had a resistant biofilm structure between all tested bacteria (16.41 %). The GC-MS analysis revealed that five active compounds were present in the extract of this plant. Discussion and conclusion: The data obtained in this study confirmed that H.officinalis extract inhibit growth and biofilm formation of some pathogenic bacteria. It can be proposed for future studies that the compounds of this plant used for design a antimicrobial agent

Physical, plant growth regulators and TiO2 nanoparticles priming treatments to improve seed germination of endangered asafoetida (Ferula assafoetida L.)

Purpose: Ferula assafoetida (L.) is one of the most important medicinal plants with many applications in food, pharmaceutical and cosmetic industries.  It has been endangered due to overharvesting from natural habitat and long period of seed dormancy. Knowledge of seed germination behavior leads to the development of its conservation and cultivation. Research methods: We conducted this research as a factorial experiment in Completely Randomized Design (CRD) to evaluate seed germination in response to low temperature, plant growth regulators (kinetin, gibberellin, carrageenan as plant bio-stimulant) and TiO2 nanoparticles (TiO2 NPs). The germination percentage and rate, mean germination time, and radicle elongation were measured. Findings: The results showed that the cold (4 °C), GA3, carrageenan, kinetin and TiO2 NPs increased seeds germination rate and percentage. Maximum seed germination percentage (86% or 23% more than control) and minimum mean germination time (26 days or 12.6 days shorter than control) obtained with seeds pretreated by kinetin soaking and TiO2 NPs treatment at 4 °C. Furthermore, most treatments produced healthier and stronger radicles compared to the control which is vital for better establishment and growth. Research limitations: No limitations were found. Originality/Value: The price and demand of asafoetida products have been increased dramatically. The most important constrain to hinder reliable supply of the products is the shortage of plant or difficulty to access its products. Here, we showed the cost effective and environmentally friendly methods to provide high seeds germination with vigorous roots

Livelihood Vulnerability of Semi-Mobile Pastoral Communities to Climate Change in Arid and Semiarid of Iran

Climate change is impacting on natural resource based livelihood systems such as pastoralist communities in arid and semi-arid regions. Vulnerability to climate change refers to the potential of a system to be harmed by this external stress. The level of vulnerability of pastoral communities and the effective components determine the extent of climate change impacts on these communities and thereby help identify institutional options that have the potential to reduce their vulnerability. This study assessed climate change vulnerability of semi-mobile pastoralist communities in five main regions (Gozm, Kaht, Madan, Rochon and Jarob) of Khabr rangelands, Kerman, Iran using the Livelihood Vulnerability Index (LVI). The data comprised of primary data on seven main components including socio-demographic profile, livelihood strategies, social networks, health, food, water availability, natural disasters and climate variability which were collected through survey of 70 semi-mobile pastoral households, and secondary data on rainfall and temperature. Data were aggregated using composite LVI index and vulnerabilities of communities were compared. Results suggested that semi-mobile pastoralists in Rochon region had the highest (0.63) LVI showing relatively the greatest vulnerability to climate change impacts in terms of Socio-Demographic Profile, Livelihood Strategies and Health while Kaht region had the least (0.49) LVI showing relatively the smallest vulnerability to climate change impacts. The results of this study are useful to access pastoralist communities’ vulnerability and set risk management policies. Keywords: climate change; livelihood vulnerability index ; semi-mobile pastoralist

Combined effect of salinity and zinc nutrition on some physiological and biochemical properties of rosemary

Salinity and reducing its destructive effects on plant, soil, and water is among the most important challenges in agricultural lands. This study aims to evaluate the effect of zinc (Zn) and salinity on some physiological and biochemical properties of rosemary. A greenhouse experiment with two levels of Zn (0 and 20 mg kg−1) and salinity of sodium chloride (0, 60, and 120 mM) in a completely randomized design was used. Salinity decreased dry weight and concentrations of K, Ca, Mg, and Zn in rosemary shoots. However, it increased electrolyte leakage, shoot sodium concentration, phenolic compounds, and catalase activity. Zinc application increased rosemary dry weight by 13% and 9% under salinity levels of 0 and 60 mM NaCl, respectively. However, at higher salinity level (120 mM), it could not ameliorate the negative impact of salinity. Zinc improved the growth of rosemary under salinity stress by increasing the cell membrane stability, increasing shoot K, Ca, Mg, and Zn concentrations, and decreasing shoot Na concentration. The concentration of phenolic compounds in the leaves of rosemary grown under the salinity levels of 0 and 60 mM NaCl increased under the influence of Zn application. Nevertheless, the phenolic content remained unchanged under 120 mM NaCl salinity level. In this study, Zn addition increased catalase activity under all salinity levels. According to the results, optimum soil Zn application can be considered as an efficient and rapid solution for increasing rosemary growth and its tolerance to salinity stress

A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods

A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods

Sleep Hygiene Pattern in Medical Residents

Background and Objective: Medical residents have a crucial role in the development of the healthcare system and medical services. This study aimed to determine the quality of life, job satisfaction, and sleep hygiene pattern of the medical residents of various specialties working in teaching centers affiliated with Shahid Beheshti University of Medical Sciences, Tehran, Iran, before and after the beginning of the residency period. Materials and Methods: This cross-sectional and descriptive-analytical study with a convenience sampling method assessed 162 medical residents who filled out the research questionnaires twice during a six-month period from September 2018 to March 2018. The required data were collected using the World Health Organization Quality of Life Assessment, Job Satisfaction Questionnaire, and Sleep Hygiene Index. Results: The results of the study revealed that the quality of life (P=0.000), job satisfaction (P=0.000), and sleep hygiene pattern (P=0.000) significantly decreased in medical residents six months after starting the residency program. Quality of life was found to be lower in men than in women (P=0.000); however, in the field of specialty, no significant difference was found in terms of the relevant variables six months after starting the residency period (P>0.05). Furthermore, having more than 15 shifts per month was significantly related to decreased quality of life in medical residents (P=0.01). Conclusion: Considering the results of the present study, there is a necessity to provide programs in medical universities to improve the quality of life, job satisfaction, and sleep hygiene pattern among medical residents during their academic period

Coronary Artery Disease Diagnosis: Ranking the Significant Features Using a Random Trees Model

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models