The purpose of this study is to understand the lived experience of urban poor of the causes affecting poverty. The research method was grounded theory and the study population included the heads of urban poor households living in districts 17, 18 and 19 of Tehran Municipality. Sampling was initially purposeful and continued until the stage of theoretical saturation. As a result, the nuclear category emerged as social exclusion from the labor market. Findings based on the paradigm model of the research were divided into five axes (causal conditions, intervening conditions and contextual conditions, strategies and consequences). In this article, the causal conditions of exclusion from the labor market are discussed. According to the participants, causal conditions include four main categories: economic capital weakness, human capital weakness, unfavorable economic conditions and unfavorable employment conditions. According to the findings, many urban poor living in the study areas had a history of rural poverty, the most obvious aspect of which is the weakness of economic and human capital. Also, their employment conditions in the city are very much affected by unfavorable economic conditions (such as: economic sanctions, outbreak of coronary heart disease, mechanization of production and provision of services and the presence of foreign nationals) and unfavorable employment conditions to the extent that these conditions have led to the perpetuation of inter generational poverty.

The purpose of this study is to understand how and why of the exclusion of poor rural immigrants to Tehran in the labor market. In this research, the basic theory method is used, which is a qualitative method. The study population is the heads of urban poor households living in districts 17, 18 and 19 of Tehran, which was selected by purposive sampling and continued until theoretical saturation. After the coding process, the research findings based on the paradigm model of the research are divided into five axes: causal conditions, intervening conditions, context, strategies and consequences, and the nuclear category was obtained as social exclusion from the formal labor market. In this article, the causal conditions are discussed. According to the participants, these conditions include four main categories (weakness of economic capital, weakness of human capital, unfavorable economic conditions and unfavorable working environment conditions). The research findings show that many poor people living in the study areas, who had a rural origin, have experienced all kinds of exclusion in the formal labor market after migrating to Tehran. Meanwhile, unfavorable economic conditions such as economic sanctions, the spread of Coronavirus, mechanization of production and provision of services and the presence of foreign nationals have affected the vulnerability and exclusion of people from the formal labor market. Also, unfavorable working conditions such as low income and lack of social support and physical and psychological harm and exploitation have led to their exclusion from the labor market.

Resilience is defined as the system ability both to keep an acceptable performance level against a severe disturbance and to return to a stable condition in a suitable time. Occurrence of inconvenient weather conditions and natural disasters, which are growing in numbers and severity during the last years, has always led to damages and wide outage throughout the distribution network. Therefore, the assessment of distribution network resilience and its return ability against severe weather condition and the reduction of the vulnerability of distribution network against those events should take priority in the planning and operation of these networks. In this paper, the resilience concept and its characteristics are determined; afterwards, by modeling natural disasters such as flood and storm, a new index for resilience assessment of distribution networks in the presence of distributed generators is introduced. The proposed model has been applied to a real network and the effects of distributed generators on the resilience of distribution networks have been studied using the new introduced index.

Adverse weather conditions and natural disasters always inflict extensive losses and outages in distribution networks while the number and severity of these incidents have often been on the rise in recent years. Therefore, evaluating the resilience of the network and its reversibility in the face of adverse weather conditions and reducing the permeability of the electricity distribution network exposed to natural disasters should be among the planning priorities for the design and optimal operation of these networks. T​this study aims to construct new tie lines between the healthy part and the damaged parts of a network in the event of a possible accident to quickly restore service to the parts that have lost electricity. The paper presents an innovative iteration-based method that uses graph theory to optimize the total objective function, including the cost of constructing tie lines, the cost of reliability, and the cost of resilience. The proposed method was applied to a part of the RBTS test system, and the effect of constructing new tie lines was investigated on the resilience and reliability indicators of the network according to the cost of their construction

Introduction:

 Honeybees play an important role in pollination. However, there are many problems that threaten the life of them. Pollinators can be exposed to insecticides during their application, by contact with residues, or from the ingestion of pollen, nectar or guttation fluid containing insecticide. The increasing use of neonicotinoids means there is a greater potential for pollinators to be exposed over longer periods as systemic insecticides can be found in the pollen and nectar of plants throughout their blooming period (Ellis, 2010). Exposure to insecticides may have lethal or sub-lethal behavioral or physiological effects. The impact of imidacloprid on homing flight was evaluated in field with a 500-m-distance between feeder and hive (Bortolotti et al. 2003). At the concentration of 100 lg kg-1 foragers fed with imidacloprid-added syrup returned to the hive, but this treatment caused a temporary inhibition of the foraging activity, lasting more than 5 h. Foragers fed with 500 and 1000 lg kg-1 of imidacloprid were seen neither at the hive nor at the feeding site, for the 24 h after the treatment (Bortolotti et al. 2003). Decourtye et al (2011) have shown how the RFID device can be used to study the effects of pesticides on both the behavioral traits and the lifespan of bees.In this context, they have developed a method under tunnel to automatically record the displacements of foragers individualized with RFID tags and to detect the alteration of the flight pattern between an artificial feeder and the hive. Fipronil was selected as test substance due to the lack of information on the effects of this insecticide on the foraging behavior of free-flying bees. They showed that oral treatment of 0.3 ng of fipronil per bee (LD50/20) reduced the number of foraging trips.Therefore, the aim of this study was to monitoring and determination honeybee’s behavior in exposure to pesticide using data mining techniques.

Materials and Methods:

 Three smart beehive systems developed to monitoring of hive internal conditions. Therefore, each beehive equipped with temperature and humidity (HDC1080, China), vibration (MPU6050, China), and CO2 (CCS811, China) sensors. Data was collected during spraying time for 48 hours and different features of vibration signal in two time-frequency and frequency domains were extracted by MFCC (Mel-Frequency Cepstral Coefficient) algorithm. After that, the most significant features were selected using PCA (Principle Component Analysis) which has been used specifically for extracting information from correlation matrices. Since the spectral dataforms the array of correlated variables containing overlapped information, this approach makes it possible to extractuseful information from high-dimensional data. To choose thenumber of components the cross-validationmethod was used. The extracted principal components wereused as the input variables for the classification model. In this paper, support vector machine with different kernel function including linear, polynomial, MLP, RBF, and quadratic was applied for performing classification.

Results and discussion:

 According to the MFCC of internal vibration results, there were dramatic changes in the range of 1800 to 2200 Hz in the time of spraying; also, Spectrogram of MFCC coefficients for the X component acceleration shown intensity of 350 in the frequency of 2000 Hz and time range of 60 to 120 minutes; besides, humidity (8 to 18 %), the amount of CO2 (450 to 530 ppm) and temperature (35 to 39 C) increased during this time.To reduce the dimensionality of data five PCs with minimum estimated mean squared prediction error (0.078) were selected based on Monte Carlo method and used in classifier. Among the five kernels (RBF, linear, MLP, Polynomial, Quadratic), RBF could recognize normal and infected colony with identification rate of 100% and 90%, respectively.

Conclusions :

According to the results temperature, humidity, CO2, and vibration sensors can recognize internal condition of bee hive. Vibration features of honey bees movements were extracted using MFCC followed by PCA in frequency-time domain. Five PCs was selected by cross-validation method and RBF kernel was the best kernel with identification rate of 100% and 90% for normal and infected beehive, respectively. Generally, the vibration signals (that were recorded by acceleration sensor) have shown the best result compare to temperature, CO2, and humidity sensors. It is worth nothing that the use of two temperature and humidity sensors is necessary to monitor and control of beehive internal conditions.