Dark fermentative hydrogen production: Potential of food waste as future energy needs.

Globally, food waste (FW) is found to be one of the major constituents creating several hurdles in waste management. On the other hand, the energy crisis is increasing and the limited fossil fuel resources available are not sufficient for energy needed for emerging population. In this context, biohydrogen production approach through valorization of FW is emerging as one of the sustainable and eco-friendly options. The present review explores FW sources, characteristics, and dark fermentative production of hydrogen along with its efficiency. FW are highly biodegradable and rich in carbohydrates which can be efficiently utilized by anaerobic bacteria. Based on the composition of FW, several pretreatment methods can be adapted to improve the bioavailability of the organics. By-products of dark fermentation are organic acids that can be integrated with several secondary bioprocesses. The versatility of secondary products is ranging from energy generation to biochemicals production. Integrated approaches facilitate in enhanced energy harvesting along with extended wastewater treatment. The review also discusses various parameters like pH, temperature, hydraulic retention time and nutrient supplementation to enhance the process efficiency of biohydrogen production. The application of solid-state fermentation (SSF) in dark fermentation improves the process efficiency. Dark fermentation as the key process for valorization and additional energy generating process can make FW the most suitable substrate for circular economy and waste based biorefinery.

Applications of Neural Network-Based Plan-Cancer Method for Primary Diagnosis of Mesothelioma Cancer.

"Malignant mesothelioma (MM)" is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is "malignant mesothelioma." Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination "epithelial/mesenchymal appearance of MM," however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful "Malignant Mesothelioma illness" identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support "Vector Machine, Neural Network, and Decision Tree" are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.