With the increasing number of immunocompromised hosts, the epidemiological characteristics of fungal infections have undergone enormous changes worldwide, including in China. In this paper, we reviewed the existing data on mycosis across China to summarize available epidemiological profiles. We found that the general incidence of superficial fungal infections in China has been stable, but the incidence of tinea capitis has decreased and the transmission route has changed. By contrast, the overall incidence of invasive fungal infections has continued to rise. The occurrence of candidemia caused by Candida species other than C. albicans and including some uncommon Candida species has increased recently in China. Infections caused by Aspergillus have also propagated in recent years, particularly with the emergence of azole-resistant Aspergillus fumigatus. An increasing trend of cryptococcosis has been noted in China, with Cryptococcus neoformans var. grubii ST 5 genotype isolates as the predominant pathogen. Retrospective studies have suggested that the epidemiological characteristics of Pneumocystis pneumonia in China may be similar to those in other developing countries. Endemic fungal infections, such as sporotrichosis in Northeastern China, must arouse research, diagnostic, and treatment vigilance. Currently, the epidemiological data on mycosis in China are variable and fragmentary. Thus, a nationwide epidemiological research on fungal infections in China is an important need for improving the country's health.

Primary cutaneous cryptococcosis (PCC) has been confirmed as a distinct clinical entity with secondary cutaneous cryptococcosis from systematic infection since 2003. Although it has been confirmed as a distinct clinical entity, little has progressed on PCC in immunocompetent hosts compared to their immunocompromised counterpart. We reviewed the literature on cases of PCC in immunocompetent patients from 2004 to 2014, and 21 cases from 16 reports were identified. Males are more likely to develop PCC infections, with a ratio of 17:4 male to female. These patients were found to be almost all senior population except for patients from Asia. Asymptomatic or moderate itching manifesting in a painful nodule is the most common presentation, although there is no typical clinical manifestation recorded. Upper limbs are the most common site of infection, accounting for 71.4 % of all patients. Of the 12 identified isolates, 6 strains are identified as C. neoformans, 5 as C. gattii, and 1 as C.laurentii. Fluconazole was used in 10 cases; however, only 80 % of the 10 cases could confirm that fluconazole was effective in clearing the infections. Interestingly although not approved as a treatment option, Itraconazole was effective in the seven cases it was used to treat cryptococcosis, with a dosage range of 100-400 mg/d and duration from 3 to 6 months. Even though the prognosis of these patients was generally good, more data are need to determine which antifungal azole is the better treatment option and whether primary skin infections could disseminate to systematic infection.

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Fire detection technology based on video images can avoid many flaws in conventional methods and detect fires. To achieve this, the support vector machine (SVM) method in machine learning theory has unique advantages, while rough set (RS) theory and SVM complement each other in application. Thus, a new classifier could be created by organically combining these methods to identify fires and provide fire warnings, yielding excellent noise suppression and promotion. Therefore, in this study, an RS is used as the front-end system for the SVM method, yielding improved performance than only SVM. Recognition time is reduced, and recognition efficiency is improved. Experiments show that the RS-SVM classifier model based on parameter optimization proposed in this paper mitigates deficiencies in overfitting and determining local extremum with excellent reliability and stability, and enhances the forecast accuracy of fires. The method also reduces false fire-detection alarms and uses fire feature selection in virtual reality (VR) video images and fire detection and recognition.

Energy is a critical and challenging issue for battery-constrained devices, especially for mobile ad hoc stations, which are responsible for relaying data packets for neighbor nodes. Therefore, considerable research has been devoted to the research of energy consumption efficiency. Care has been taken not only to reduce the overall energy consumption but also to balance individual battery usage, since unbalanced energy consumption will result in earlier node failure for overloaded nodes, and then lead to network partition and reduced network lifetime. This paper presents a novel on demand routing algorithm named Energy Aware Reliable Routing (EARR) to avoid the over usage of hot spots, reduce the route-reconstructions due to residual energy shortages, and finally prolong network lifetime. In the method, only nodes with sufficient residual energy to complete the task will take part in the propagation of route request (RREQ), therefore only valid path has the potential to transmit data. Simulation results verified that EARR can improve the network performance in terms of average route discovery time, normalized routing load, as well distribution of energy consumption. Besides, it is more robust to energy estimation error than existing methods.