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In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.

The working temperature of the photovoltaic panel (PV) represents a key influential parameter of the system due to temperature degradation of electrical efficiency. To increase the overall efficiency of the photovoltaic system, it is necessary to make better use of the inevitable waste heat. The experimental research focused on the temperature distribution of two identical PV panels in Mediterranean climate conditions, with one panel being the referent and the other incorporating a passive/active cooling system, i.e. photovoltaic-thermal (PVT) collector. Separate water-cooled containers filled with organic phase change material (lard) were added to the back of the commercially available photovoltaic panel to create a photovoltaic- thermal collector. Temperatures on the surfaces of both panels were continuously monitored depending on insolation whereby a smart control system was implemented in the PVT collector to maximize both electric and thermal output. The measurement results revealed the effectiveness of lard in thermal management systems, considering that the surfaces of the PVT collector during the peak heat load were always colder than the surfaces of the referent PV panel, i.e. up to 5.1 °C. For the considered period of 10 days, the electrical efficiency of the novel PVT collector was 3.2 % higher than the conventional PV panel, and also an additional 45, 208.7 Wh of useful heat was generated. The collected experimental data combined with the numerical approach is necessary for the iterative improvement of the smart control system, i.e. fine-tuning the active component of the PVT collector. The results of the experimental research provide a benchmark for the numerical model and consequently the optimization of this novel PVT concept.

Transient liquefaction of organic phase change material (PCM) based on industrially processed pork fat, i.e. lard, was studied in this work. Two-dimensional computational fluid dynamics (CFD) model for transient heat transfer was developed and numerically tested. The numerical analysis provides insight into the intensity of heat transfer and the rate of phase transition of a new relatively inexpensive environmentally friendly phase change material. The rectangular well- insulated domain in which the PCM is located was exposed to a continuous plate heat source. The developed model predicts heat dissipation well and has proven to be relatively robust in terms of the time step as a result of high-quality hexahedral mesh. The time required for the complete phase transition of lard and the limitations of the commercial phase transition model was determined. The developed model represents the first step toward a low-cost experimental setup purposely designed for latent heat investigations. The numerical analysis integrated with the experimental approach will help to establish a new procedure for determining the latent heat of different PCMs in the future.

The majority of market available photovoltaic (PV) technologies are silicon based PV technologies with a current market share more than 95%. Besides a relatively high investment cost and modest overall energy conversion efficiency, one of the main drawbacks related to siliceous based PV technologies is associated with performance degradation caused by the elevated PV panel operating temperatures during their regular operation. Elevated operating temperatures in the long term are also increasing performance degradation rate, which finally reduces the lifetime of the PV panel. Therefore, in the past two decades, different cooling techniques have been developed for SI- based PVs in order to check potential increase in performance. Based on existing research findings, different active and passive cooling strategies for PVs are briefly elaborated and discussed from techno- economic and environmental point of the view.

The nanoparticle-enhanced phase change material (NEPCM) has a good potential for improving the energy conversion efficiency of photovoltaic (PV) system by increasing the thermal conductivity of phase change material. In this paper, a photovoltaic panel coupled with nanoparticle- enhanced phase change material (PV-NEPCM) was numerically investigated considering different PCM melting temperatures (ranging from 5 to 50°C with 1°C intervals), PCM layer thicknesses the range of 1-50 mm with 1 mm intervals) and nanoparticle volume fractions (1%, 3% and 5%). Measured monthly-averaged climatic data for Ankara was used to analyze the effect of investigated parameters on the conversion efficiency, cell temperature and electricity output of PV-NEPCM system. The results showed that the combination which provides the highest improvement (1.72%) is NEPCM with PCM melting temperature of 20oC, PCM thickness of 50 mm and nanoparticle volume fraction of 5%. The total annual electricity output of PV panel, 9585 Wh/m2year, can be increased to 9750 Wh/m2year by incorporating NEPCM with optimized parameter.