ANALISIS KINERJA PANEL SURYAAKIBAT PENDINGINAN AKTIF

Energi surya dapat menghasilkan dua jenis energi yaitu energi cahaya dan energi panas. Kedua energi tersebut dapat dimanfaatkan dalam berbagai aktivitas manusia, salah satunya untuk menghasilkan energi listrik. Kajian awal menyimpulkan bahwa kenaikan temperatur permukaan sebesar 1oC akan menurunkan daya keluaran dan efisiensi panel surya sebesar 0,5%. Peningkatan efisiensi panel surya dapat dilakukan dengan menurunkan atau mempertahankan temperatur panel surya pada kondisi mendekati 25oC dengan sistem pendinginan aktif maupun pasif. Maka itu pada penelitian ini akan melihat kinerja panel surya akibat adanya pendinginan aktif dengan tujuan untuk menganalisis pengaruh perbandingan watercoolant dan air murni sebagai pendingin aktif pada panel surya 130 Wp untuk memperoleh nilai efisiensi panel surya yang optimum. Sudut kemiringan panel surya adalah 10o serta Kecepatan aliran fluida konstan 100 L/Jam. Perbandingan campuran antara watercoolant dan air murni adalah sebagai berikut; 0:1 (air murni saja), 1:20, 1:40, 1:60, 1:80, 1:100. Pada perbandingan air murni, efisiensi meningkat sebesar 16,27%. Perbandingan 1:20, efisiensi meningkat sebesar 16,96%. Perbandingan 1:40, efisiensi meningkat sebesar 17,69%. Perbandingan 1:60, efisiensi meningkat sebesar 17,93%. Perbandingan 1:80, efisiensi meningkat sebesar 22,99%. Perbandingan 1:100, efisiensi meningkat sebesar 16,14%. Dari hasil pengolahan data dan pembahasan, dapat diperoleh bahwa peningkatan efisiensi yang optimal terdapat pada perbandingan 1:80

Thermal Characterization of Dynamic Silicon Cantilever Array Sensors by Digital Holographic Microscopy

In this paper, we apply a digital holographic microscope (DHM) in conjunction with stroboscopic acquisition synchronization. Here, the temperature-dependent decrease of the first resonance frequency (S1(T)) and Young’s elastic modulus (E1(T)) of silicon micromechanical cantilever sensors (MCSs) are measured. To perform these measurements, the MCSs are uniformly heated from T0 = 298 K to T = 450 K while being externally actuated with a piezo-actuator in a certain frequency range close to their first resonance frequencies. At each temperature, the DHM records the time-sequence of the 3D topographies for the given frequency range. Such holographic data allow for the extracting of the out-of-plane vibrations at any relevant area of the MCSs. Next, the Bode and Nyquist diagrams are used to determine the resonant frequencies with a precision of 0.1 Hz. Our results show that the decrease of resonance frequency is a direct consequence of the reduction of the silicon elastic modulus upon heating. The measured temperature dependence of the Young’s modulus is in very good accordance with the previously-reported values, validating the reliability and applicability of this method for micromechanical sensing applications

Precise control of thermal conductivity at the nanoscale through individual phonon-scattering barriers

International audienceThe ability to precisely control the thermal conductivity (κ) of a material is fundamental in the development of on-chip heat management or energy conversion applications. Nanostructuring permits a marked reduction of κ of single-crystalline materials, as recently demonstrated for silicon nanowires. However, silicon-based nanostructured materials with extremely low κ are not limited to nanowires. By engineering a set of individual phonon-scattering nanodot barriers we have accurately tailored the thermal conductivity of a single-crystalline SiGe material in spatially defined regions as short as ∼15 nm. Single-barrier thermal resistances between 2 and 4×10−9 m2 K W−1 were attained, resulting in a room-temperature κ down to about 0.9 W m−1 K−1, in multilayered structures with as little as five barriers. Such low thermal conductivity is compatible with a totally diffuse mismatch model for the barriers, and it is well below the amorphous limit. The results are in agreement with atomistic Green’s function simulations

Automated Alphabet Reduction for Protein Datasets

<p>Abstract</p> <p>Background</p> <p>We investigate automated and generic alphabet reduction techniques for protein structure prediction datasets. Reducing alphabet cardinality without losing key biochemical information opens the door to potentially faster machine learning, data mining and optimization applications in structural bioinformatics. Furthermore, reduced but informative alphabets often result in, e.g., more compact and human-friendly classification/clustering rules. In this paper we propose a robust and sophisticated alphabet reduction protocol based on mutual information and state-of-the-art optimization techniques.</p> <p>Results</p> <p>We applied this protocol to the prediction of two protein structural features: contact number and relative solvent accessibility. For both features we generated alphabets of two, three, four and five letters. The five-letter alphabets gave prediction accuracies statistically similar to that obtained using the full amino acid alphabet. Moreover, the automatically designed alphabets were compared against other reduced alphabets taken from the literature or human-designed, outperforming them. The differences between our alphabets and the alphabets taken from the literature were quantitatively analyzed. All the above process had been performed using a primary sequence representation of proteins. As a final experiment, we extrapolated the obtained five-letter alphabet to reduce a, much richer, protein representation based on evolutionary information for the prediction of the same two features. Again, the performance gap between the full representation and the reduced representation was small, showing that the results of our automated alphabet reduction protocol, even if they were obtained using a simple representation, are also able to capture the crucial information needed for state-of-the-art protein representations.</p> <p>Conclusion</p> <p>Our automated alphabet reduction protocol generates competent reduced alphabets tailored specifically for a variety of protein datasets. This process is done without any domain knowledge, using information theory metrics instead. The reduced alphabets contain some unexpected (but sound) groups of amino acids, thus suggesting new ways of interpreting the data.</p

Evolving cell models for systems and synthetic biology

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models

Non-random distribution of deleterious mutations in the DNA and protein-binding domains of IRF6 are associated with Van Der Woude syndrome

Background: The development of the face occurs during the early days of intrauterine life by the formation of facial processes from the first Pharyngeal arch. Derangement in these well-organized fusion events results in Orofacial clefts (OFC). Van der Woude syndrome (VWS) is one of the most common causes of syndromic cleft lip and/or palate accounting for 2% of all cases. Mutations in the IRF6 gene account for 70% of cases with the majority of these mutations located in the DNA-binding (exon 3, 4) or protein-binding domains (exon 7-9). The current study was designed to update the list of IRF6 variants reported for VWS by compiling all the published mutations from 2013 to date as well as including the previously unreported VWS cases from Africa and Puerto Rico.Methods: We used PubMed with the search terms; "Van der Woude syndrome," "Popliteal pterygium syndrome," "IRF6," and "Orofacial cleft" to identify eligible studies. We compiled the CADD score for all the mutations to determine the percentage of deleterious variants.Results: Twenty-one new mutations were identified from nine papers. The majority of these mutations were in exon 4. Mutations in exon 3 and 4 had CADD scores between 20 and 30 and mutations in exon 7-9 had CADD scores between 30 and 40. The presence of higher CADD scores in the protein-binding domain (exon 7-9) further confirms the crucial role played by this domain in the function of IRF6. In the new cases, we identified five IRF6 mutations, three novel missense mutations (p.Phe36Tyr, p.Lys109Thr, and p.Gln438Leu), and two previously reported nonsense mutations (p.Ser424*and p.Arg250*).Conclusion: Mutations in the protein and DNA-binding domains of IRF6 ranked among the top 0.1% and 1% most deleterious genetic mutations, respectively. Overall, these findings expand the range of VWS mutations and are important for diagnostic and counseling purposes.</p

Sponge spicules as blueprints for the biofabrication of inorganic–organic composites and biomaterials

While most forms of multicellular life have developed a calcium-based skeleton, a few specialized organisms complement their body plan with silica. However, of all recent animals, only sponges (phylum Porifera) are able to polymerize silica enzymatically mediated in order to generate massive siliceous skeletal elements (spicules) during a unique reaction, at ambient temperature and pressure. During this biomineralization process (i.e., biosilicification) hydrated, amorphous silica is deposited within highly specialized sponge cells, ultimately resulting in structures that range in size from micrometers to meters. Spicules lend structural stability to the sponge body, deter predators, and transmit light similar to optic fibers. This peculiar phenomenon has been comprehensively studied in recent years and in several approaches, the molecular background was explored to create tools that might be employed for novel bioinspired biotechnological and biomedical applications. Thus, it was discovered that spiculogenesis is mediated by the enzyme silicatein and starts intracellularly. The resulting silica nanoparticles fuse and subsequently form concentric lamellar layers around a central protein filament, consisting of silicatein and the scaffold protein silintaphin-1. Once the growing spicule is extruded into the extracellular space, it obtains final size and shape. Again, this process is mediated by silicatein and silintaphin-1, in combination with other molecules such as galectin and collagen. The molecular toolbox generated so far allows the fabrication of novel micro- and nanostructured composites, contributing to the economical and sustainable synthesis of biomaterials with unique characteristics. In this context, first bioinspired approaches implement recombinant silicatein and silintaphin-1 for applications in the field of biomedicine (biosilica-mediated regeneration of tooth and bone defects) or micro-optics (in vitro synthesis of light waveguides) with promising results

Depletion of Dendritic Cells Enhances Innate Anti-Bacterial Host Defense through Modulation of Phagocyte Homeostasis

Dendritic cells (DCs) as professional antigen-presenting cells play an important role in the initiation and modulation of the adaptive immune response. However, their role in the innate immune response against bacterial infections is not completely defined. Here we have analyzed the role of DCs and their impact on the innate anti-bacterial host defense in an experimental infection model of Yersinia enterocolitica (Ye). We used CD11c-diphtheria toxin (DT) mice to deplete DCs prior to severe infection with Ye. DC depletion significantly increased animal survival after Ye infection. The bacterial load in the spleen of DC-depleted mice was significantly lower than that of control mice throughout the infection. DC depletion was accompanied by an increase in the serum levels of CXCL1, G-CSF, IL-1α, and CCL2 and an increase in the numbers of splenic phagocytes. Functionally, splenocytes from DC-depleted mice exhibited an increased bacterial killing capacity compared to splenocytes from control mice. Cellular studies further showed that this was due to an increased production of reactive oxygen species (ROS) by neutrophils. Adoptive transfer of neutrophils from DC-depleted mice into control mice prior to Ye infection reduced the bacterial load to the level of Ye-infected DC-depleted mice, suggesting that the increased number of phagocytes with additional ROS production account for the decreased bacterial load. Furthermore, after incubation with serum from DC-depleted mice splenocytes from control mice increased their bacterial killing capacity, most likely due to enhanced ROS production by neutrophils, indicating that serum factors from DC-depleted mice account for this effect. In summary, we could show that DC depletion triggers phagocyte accumulation in the spleen and enhances their anti-bacterial killing capacity upon bacterial infection