Search Results (5)

Background: Psoriasis and atopic dermatitis are chronic skin diseases found all over the world that cause a lot of suffering to patients. Objectives: The aim of this study was to answer the following questions: whether people suffering from psoriasis and AD have greater problems with recognizing emotions, the effectiveness of attention and memory processes, and whether they use different strategies of coping with stress than healthy people. Methods: This study involved 90 patients, including 30 patients with psoriasis, 30 patients with AD and 30 healthy patients, aged 21 to 63 years, including 54 women and 36 men. This study used a battery of the CANTAB Cognitive Tests, Mini-COPE Questionnaire Inventory, Toronto Alexithymia Scale TAS Questionnaire, Psoriasis Area and Severity Index, and Eczema Area and Severity Index. Results: People with psoriasis and AD had higher total scores on the alexithymia scale and had greater difficulty in identifying and verbalizing emotions. People with psoriasis and AD are less likely to choose the correct stimulus and achieve a shorter length of the sequence that should be remembered. Psoriasis patients with more severe symptoms are less likely to use the strategy of a sense of humor in stressful situations. AD patients with more severe symptoms are less likely to use strategies of operative thinking, denial and self-blame, and the strategy of seeking instrumental support is used more often. Conclusions: Patients with psoriasis and AD require a holistic approach; in addition to dermatological treatment, psychological support, psychotherapeutic support and possible psychiatric treatment are recommended. Full article

As climate change and human activity increase the likelihood of devastating wildfires, the need for early fire detection methods is inevitable. Although, it has been shown that deep learning and artificial intelligence can offer a solution to this problem, there is still a lot of room for improvement. In this research, two new deep learning approaches to fire detection are developed and investigated utilizing pre-trained ResNet-50 and Xception for feature extraction with a detailed comparison against support vector machine (SVM), ResNet-50, Xception, and MobileViT architectures. Each architecture was tuned utilizing hyperparameter searches and trials to seek ideal combinations for performance. To address the under-representation of desert features in the current fire detection datasets, we have created a new dataset. This novel dataset, Utah Desert Fire, was created using controlled fires and aerial imaging with a DJI Mini 3 Pro drone. The proposed modified ResNet-50 architecture achieved the best performance on the Utah Desert Fire dataset, reaching 100% detection accuracy. To further compare the proposed methods, the popular forest fire detection dataset, DeepFire, was deployed with resulting performance analyzed against most recent literature. Here, our proposed modified Xception model outperformed latest publications attaining 99.221% accuracy. The performance of the proposed solutions show an increase in classification accuracy which can be leveraged for the identification of both desert and forest fires. Full article

Immunotherapy has made great progress in recent years, yet the efficacy of solid tumors remains far less than expected. One of the main hurdles is to overcome the immune-suppressive tumor microenvironment (TME). Among all cells in TME, tumor-associated macrophages (TAMs) play pivotal roles because of their abundance, multifaceted interactions to adaptive and host immune systems, as well as their context-dependent plasticity. Underlying the highly plastic characteristic, lots of research interests are focused on repolarizing TAMs from M2-like pro-tumor phenotype towards M1-like antitumoral ones. Nanotechnology offers great opportunities for targeting and modulating TAM polarization to mount the therapeutic efficacy in cancer immunotherapy. Here, this mini-review highlights those emerging nano-approaches for TAM repolarization in the last three years. Full article

In this article, a fuzzy logic model is proposed for more precise hourly electrical power demand modeling in New England. The issue that exists when considering hourly electrical power demand modeling is that these types of plants have a large amount of data. In order to obtain a more precise model of plants with a large amount of data, the main characteristics of the proposed fuzzy logic model are as follows: (1) it is in accordance with the conditions under which a fuzzy logic model and a radial basis mapping model are equivalent to obtain a new scheme, (2) it uses a combination of the descending gradient and the mini-lots approach to avoid applying the descending gradient to all data. Full article

UN Sustainable Development Goal (SDG) 7 states that access to affordable, reliable, sustainable, and modern energy should be provided for all by 2030. Mozambique is one of the poorest countries on earth but has abundant supply of energy resources. The electrification rate, however, has been slow. Rural electrification is hampered by lack of basic infrastructure, institutional barriers, and low ability and willingness to pay for energy services. Thus, there is a general gap between electricity supply and demand. In view of this, the present paper investigates supply and demand of electricity for a typical rural case study area in Mozambique. We suggest a nexus approach to improve water, energy, and food security initiated at a local level. Households in the investigated case study area can be connected to a mini-grid hydropower system that allows for energy production to 80–200 households. To increase the economic profitability of the mini-grid hydropower system we suggest including small-scale industry. For the studied rural village, this would be constituted by a small-scale factory for milling of corn and other cereals. Electricity produced can thus be used for food processing but also for pumping water supply to households for domestic use and small-scale irrigated farming lots. The results indicate that the villagers have a real payment capacity of between 8 and 19 USD per MWh of generated hydropower. Benefits of rural electrification are direct. It contributes to improved living conditions for households and provides better nexus security and sustainable development within healthcare, education, and small-scale business development. Full article