Audit Energi pada Data Center Kampus untuk Efisiensi Energi Berbasis Digital Twin

The research was developed using digital twin techniques to predict thermal loads through real-time data on the HVAC system in the data center. The physical device system was digitalized using IoT (Internet of Things) technology, and through this technology, a digital space was created to represent the prediction model. Instrumentation for data acquisition and real-time monitoring systems was created using IoT techniques, as well as an analysis of the performance of the data center cooling system. The aim of this research was to obtain thermal load predictions for the data center energy system and then analyze them using the heat balance method to determine the ratio of thermal load to the performance (cooling capacity) of the existing data center cooling devices. This was done to determine the potential for energy savings. The average predicted thermal load was 30.66 kW/h on October 25, 2022, and 29.88 kW/h on October 26, 2022. Therefore, the heat balance value against the nominal cooling capacity of the installed cooling devices was 40.95% for PAC 1 and 49.21% for PAC 2.Penelitian dikembangkan menggunakan teknik digital twin untuk membuat prediksi beban termal melalui data real time pada sistem HVAC di data center. Digitalisasi sistem perangkat fisik dilakukan dengan menggunakan teknologi IoT (Internet of Things), melalui teknologi IoT ini ruang digital dibuat untuk merepresentasikan model prediksi. Membuat instrumentasi akuisisi data dan sistem pemantauan real time melalui teknik IoT serta analisis kinerja sisem pendingin data center. Tujuan yang hendak dicapai dalam penelitian ini adalah mendapatkan prediksi beban termal sistem energi data center, kemudian dianalisis menggunakan metode heat balance agar dapat diketahui rasio beban termal terhadap kinerja (kapasitas pendinginan) perangkat pendingin data center yang ada. Hal ini dilakukan agar dapat mengetahui potensi penghematan energi listrik. Hasil prediksi beban termal didapat nilai rata-rata 30,66 kW/jam untuk tanggal 25 Oktober 2022 dan 29,88 kW/jam untuk tanggal 26 Oktober 2022. Sehingga nilai kesetimbangan beban panas (heat balance) terhadap kapasitas pendinginan nominal perangkat pendingin terpasang yaitu 40, 95 % untuk PAC 1 dan 49,21 % untuk PAC 2

Assessing Root System Architecture of Wheat Seedlings Using A High-Throughput Root Phenotyping System

Background and aims Root system architecture is a vital part of the plant that has been shown to vary between species and within species based on response to genotypic and/or environmental influences. The root traits of wheat seedlings is critical for the establishment and evidently linked to plant height and seed yield. However, plant breeders have not efficiently developed the role of RSA in wheat selection due to the difficulty of studying root traits. Methods We set up a root phenotyping platform to characterize RSA in 34 wheat accessions. The phenotyping pipeline consists of the germination paper-based moisture replacement system, image capture units, and root-image processing software. The 34 accessions from two different wheat ploidy levels (hexaploids and tetraploids), were characterized in ten replicates. A total of 19 root traits were quantified from the root architecture generated. Results This pipeline allowed for rapid screening of 340 wheat seedlings within 10days. Also, at least one line from each ploidy (6x and 4x) showed significant differences (P \u3c 0.05) in measured traits except in mean seminal count. Our result also showed strong correlation (0.8) between total root length, maximum depth and convex hull area. Conclusions This phenotyping pipeline has the advantage and capacity to increase screening potential at early stages of plant development leading to characterization of wheat seedling traits that can be further examined using QTL analysis in populations generated from the examined accessions

Variation Analysis of Root System Development in Wheat Seedlings Using Root Phenotyping System

Root system architecture is a vital part of the plant that has been shown to vary between species and within species based on response to genotypic and/or environmental influences. The root traits of wheat seedlings are critical for their establishment in soil and evidently linked to plant height and seed yield. However, plant breeders have not efficiently developed the role of RSA in wheat selection due to the difficulty of studying root traits. We set up a root phenotyping platform to characterize RSA in 34 wheat accessions. The phenotyping pipeline consists of the germination paper-based moisture replacement system, image capture units, and root-image processing software. The 34 accessions from two different wheat ploidy levels (hexaploids and tetraploids), were characterized in ten replicates. A total of 19 root traits were quantified from the root architecture generated. This pipeline allowed for rapid screening of 340 wheat seedlings within 10 days. At least one line from each ploidy (6× and 4×) showed significant differences (p \u3c 0.05) in measured traits, except for mean seminal count. Our result also showed a strong correlation (0.8) between total root length, maximum depth and convex hull area. This phenotyping pipeline has the advantage and capacity to increase screening potential at early stages of plant development, leading to the characterization of wheat seedling traits that can be further examined using QTL analysis in populations generated from the examined accessions

Sentiment Search: Make the Internet your Focus Group

This is a final project report under the course CSE 6242 Data Vis & AnalysisSentiment Analysis has been used to identify changing moods of populations. In this project, we have analysed how sentiments revolve over topics from significant world events across social media platform (Facebook, Reddit, Twitter) and news sources (CNN, The New York Times, The Guardian). We have created an interactive visualization tool that allows to filter data on specific keywords and dates, and visualize time series sentiments along with the top words used in posts. This dashboard could potentially be used by businesses or political campaigns to analyze the effect of marketing strategies on public sentiment regarding their product, or to analyze the social climate surrounding certain ideas and issues on multiple platforms. Future steps could include a dynamic rendering of sentiments with new media posts using faster, more efficient algorithms

SnTox3 Acts in Effector Triggered Susceptibility to Induce Disease on Wheat Carrying the Snn3 Gene

The necrotrophic fungus Stagonospora nodorum produces multiple proteinaceous host-selective toxins (HSTs) which act in effector triggered susceptibility. Here, we report the molecular cloning and functional characterization of the SnTox3-encoding gene, designated SnTox3, as well as the initial characterization of the SnTox3 protein. SnTox3 is a 693 bp intron-free gene with little obvious homology to other known genes. The predicted immature SnTox3 protein is 25.8 kDa in size. A 20 amino acid signal sequence as well as a possible pro sequence are predicted. Six cysteine residues are predicted to form disulfide bonds and are shown to be important for SnTox3 activity. Using heterologous expression in Pichia pastoris and transformation into an avirulent S. nodorum isolate, we show that SnTox3 encodes the SnTox3 protein and that SnTox3 interacts with the wheat susceptibility gene Snn3. In addition, the avirulent S. nodorum isolate transformed with SnTox3 was virulent on host lines expressing the Snn3 gene. SnTox3-disrupted mutants were deficient in the production of SnTox3 and avirulent on the Snn3 differential wheat line BG220. An analysis of genetic diversity revealed that SnTox3 is present in 60.1% of a worldwide collection of 923 isolates and occurs as eleven nucleotide haplotypes resulting in four amino acid haplotypes. The cloning of SnTox3 provides a fundamental tool for the investigation of the S. nodorum-wheat interaction, as well as vital information for the general characterization of necrotroph-plant interactions.This work was supported by USDA-ARS CRIS projects 5442-22000-043-00D and 5442-22000-030-00D

Identification of a candidate gene for a QTL for spikelet number per spike on wheat chromosome arm 7AL by high-resolution genetic mapping.

Key messageA high-resolution genetic map combined with haplotype analyses identified a wheat ortholog of rice gene APO1 as the best candidate gene for a 7AL locus affecting spikelet number per spike. A better understanding of the genes controlling differences in wheat grain yield components can accelerate the improvements required to satisfy future food demands. In this study, we identified a promising candidate gene underlying a quantitative trait locus (QTL) on wheat chromosome arm 7AL regulating spikelet number per spike (SNS). We used large heterogeneous inbred families ( > 10,000 plants) from two crosses to map the 7AL QTL to an 87-kb region (674,019,191-674,106,327 bp, RefSeq v1.0) containing two complete and two partial genes. In this region, we found three major haplotypes that were designated as H1, H2 and H3. The H2 haplotype contributed the high-SNS allele in both H1 × H2 and H2 × H3 segregating populations. The ancestral H3 haplotype is frequent in wild emmer (48%) but rare (~ 1%) in cultivated wheats. By contrast, the H1 and H2 haplotypes became predominant in modern cultivated durum and common wheat, respectively. Among the four candidate genes, only TraesCS7A02G481600 showed a non-synonymous polymorphism that differentiated H2 from the other two haplotypes. This gene, designated here as WHEAT ORTHOLOG OF APO1 (WAPO1), is an ortholog of the rice gene ABERRANT PANICLE ORGANIZATION 1 (APO1), which affects spikelet number. Taken together, the high-resolution genetic map, the association between polymorphisms in the different mapping populations with differences in SNS, and the known role of orthologous genes in other grass species suggest that WAPO-A1 is the most likely candidate gene for the 7AL SNS QTL among the four genes identified in the candidate gene region

Genome‑wide association analyses of leaf rust resistance in cultivated emmer wheat

Leaf rust, caused by Puccinia triticina (Pt), constantly threatens durum (Triticum turgidum ssp. durum) and bread wheat (Triticum aestivum) production worldwide. A Pt race BBBQD detected in California in 2009 poses a potential threat to durum production in North America because resistance source to this race is rare in durum germplasm. To find new resistance sources, we assessed a panel of 180 cultivated emmer wheat (Triticum turgidum ssp. dicoccum) accessions for seedling resistance to BBBQD and for adult resistance to a mixture of durum-specific races BBBQJ, CCMSS, and MCDSS in the field, and genotyped the panel using genotype-by-sequencing (GBS) and the 9 K SNP (Single Nucleotide Polymorphism) Infinium array. The results showed 24 and nine accessions consistently exhibited seedling and adult resistance, respectively, with two accessions providing resistance at both stages. We performed genome-wide association studies using 46,383 GBS and 4,331 9 K SNP markers and identified 15 quantitative trait loci (QTL) for seedling resistance located mostly on chromosomes 2B and 6B, and 11 QTL for adult resistance on 2B, 3B and 6A. Of these QTL, one might be associated with leaf rust resistance (Lr) gene Lr53, and two with the QTL previously reported in durum or hexaploid wheat. The remaining QTL are potentially associated with new Lr genes. Further linkage analysis and gene cloning are necessary to identify the causal genes underlying these QTL. The emmer accessions with high levels of resistance will be useful for developing mapping populations and adapted durum germplasm and varieties with resistance to the durum-specific races