management strategy
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Nosemosis is a serious microsporidian disease of adult European honey bees caused by the spore-forming unicellular fungi Nosema apis and Nosema ceranae. In this paper we describe the currently known techniques for nosemosis prevention and control including Good Beekeeping Practices (GBPs) and biosecurity measures (BMBs). Topics such as queen renewal, nosema-resistant bees and hygienic and control methods are described. Strong efforts are currently provided to find more a sustainable solution than the use of antibiotics. So far, it seems that the best way to approach nosemosis is given by an “integrated pest management strategy”, which foresees the contemporary application of different, specific GBPs and BMBs.

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

Mucosal melanomas (MM) are rare tumors, being less than 2% of all diagnosed melanomas, comprising a variegated group of malignancies arising from melanocytes in virtually all mucosal epithelia, even if more frequently found in oral and sino-nasal cavities, ano-rectum and female genitalia (vulva and vagina). To date, there is no consensus about the optimal management strategy of MM. Furthermore, the clinical rationale of molecular tumor characterization regarding BRAF, KIT or NRAS, as well as the therapeutic value of immunotherapy, chemotherapy and targeted therapy, has not yet been deeply explored and clearly established in MM. In this overview, focused on anorectal and genital MM as models of rare melanomas deserving of a multidisciplinary approach, we highlight the need of referring these patients to centers with experts in melanoma, anorectal and uro-genital cancers treatments. Taking into account the rarity, the poor outcomes and the lack of effective treatment options for MM, tailored research needs to be promptly promoted.