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Exposure to allergens could be either a risk factor of sensitization and nonspecific hyperresponsiveness in genetically predisposed patients or a risk of onset of asthma attack in certain allergic asthma. During the past 20 years, in western countries the houses have become higher and the number of furred pets have increased and have been more kept inside the house which makes probable that exposure to indoor aeroallergen has increased. The development of new methods of allergen measurements allows a more precise identification of allergen source and reservoirs, an assessment of allergen exposure and a monitoring of allergen eviction methods. Concerning mite allergens, controlled studies which showed a clinical efficacy are those with a global mite eviction and at least a 6 months follow-up for cat and dog allergens, high efficiency-filters air cleaners or vacuum-cleaners are able to reduce airborne cat or dog allergen levels. According to the increasing number of papers about allergen eviction, it seems logical to propose allergen eviction as "first line treatment" of allergic asthma. In the future, it would be interesting to develop biological markers to identify more accurately patients who have a clinical improvement after allergen eviction.

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The paper considers the issues of increasing the efficiency and environmental safety of cement production by using clinker dust in slurry preparation and replacing a certain part of clinker with mineral additives having pozzolanic activity. On the basis of research work carried out at the production site of the Baherden cement plant (Turkmenistan), the suitability of a Venturi scrubber to capture the dust generated in wet clinker kilns of cement plants has been established. The technological scheme for the use of the sludge generated in the scrubber in the preparation of clinker was developed. Based on the results of analyses of the main indicators of cement prepared with the addition of sludge the possibility of using the collected dust in the production of cement has been established. The optimal solution for three interrelated problems, such as increasing the efficiency of cement production, reducing the amount of harmful gases emitted into the atmosphere, and rational use of natural resources, is to reduce the specific consumption of clinker by compensating a certain part of it with mineral additives with pozzolanic activity.

Photovoltaic conversion is an optimal solution for the electrification of rural areas, especially deserts for the abundance of solar energy in these regions. In recent years, many studies have been carried out to maximize the energy productivity of a PV array system and increase its efficiency. However, the arid and semi-arid region is characterized by a climate whose parameters significantly influence the operation of PV installations. This paper presents an overview investigation of the major internal and external factors significantly affecting both the efficiency and the performance of solar cells and the power of PV systems. These factors include the type of PV material, solar radiation intensity received, cell temperature, parasitic resistances, cloud, and other shading effects, inverter efficiency, dust, module orientation, weather conditions, geographical location, and cable thickness. Simulation of a PV system has been carried out in MATLAB-SIMULINK to prove the effectiveness of the proposed modeling method. These simulation results are useful to predict the production of the PV module under real operating conditions.

Chip On Film(COF) is the key component of electronic products, and is different from Printed Circuit Board(PCB). The properties of high flexibility, thin thickness, lightweight and high wiring density make it difficult to inspect COF, especially dust particles interference. Dust particles are similar to defects, and it is hard to identify dust particles from defects, so dust particles interference of quality test is the difficulty of automatic surface defect detection. In this paper, a new method to identify dust particles is discussed from abnormal area called junction points detection and machine learning method Support Vector Machine(SVM) according to the characteristics of dust particles. As a result, a 94.8% correct rate of dust particles images identification has been achieved with the method.