Different Factors That Influence the Rise of India and Indonesia

India and Indonesia are among the largest economies in the world, and this was not something serious for China to pay attention to in the past. However, in this decade, these two countries have shown aggressive economic growth, compared to other developed and developing countries such as Russia and Mexico. India under the Modi administration launched the Digital India 2025 ambition in 2018 and a GDP target of 5 trillion USD, Indonesia under the Jokowi administration featured the Global Marine Fulcrum (GMF) and the target of becoming the 4th largest economy in the world by 2045. Both focus on many sectors, especially economic support infrastructures such as railroads, ports, and fast trains. In terms of military, India is already strong in the 4th position in the world, and Indonesia is still far below India, the 16th in the world. The current world situation is unstable, leading India to steps to strengthen ties with Western countries to stem China's growth. On the Indonesian side, it tends not to field close relations with the West and is still cooperating, both with the West and China to develop the country's potential and infrastructure. However, both of them still have the duty to become the foremost countries; India with the problem of unification, and adjusting its foreign policies to neighboring countries, and Indonesia need to finalize on innovation and domestic development. &nbsp

Statistical Modeling of Count Data with Over-Dispersion or Zero-Inflation Problems

In this study, we will analyze a supply retailing company’s data to model the relationship between their customer’s past purchase behavior to predict their future online purchase behavior. The data was divided into time periods from 2016: P1-P6(January 31st to July 30th) and P7(July 31st to August 27th ). Based on customer’s past purchase information from the P1-P6 period, such as money spent, number of cart additions, transactions type, number of unique purchase dates, number of unique purchase skus, number of page views, number browse dates, company size, and number of products purchased, we aim to find if these information could predict the customer’s purchase behavior in the P7 period, which is the number of responses the customer responded to emails sent to them during P7. With the response variable as count data, we model the data in R with the Poisson distribution regression with an offset variable. We also model the number of responses out of the number of emails sent using a logistic regression model. For the Poisson model, since there are zero inflation or over-dispersion issues in the response, hurdle model, zero-inflated-poisson (ZIP) model and zero-inflated-negative-binomial (ZINB) model would be used to handle these issues. Model comparisons among the Poisson model with an offset, logistic regression model, hurdle model, ZIP, ZINB is conducted to select the best model to fit the data using the AIC criterion and the cross-validation criterion

Layered microporous polymers by solvent knitting method

Two-dimensional (2D) nanomaterials, especially 2D organic nanomaterials with unprecedentedly diverse and controlled structure, have attracted decent scientific interest. Among the preparation strategies, the top-down approach is one of the considered low-cost and scalable strategies to obtain 2D organic nanomaterials. However, some factors of their layered counterparts limited the development and potential applications of 2D organic nanomaterials, such as type, stability, and strict synthetic conditions of layered counterparts. We report a class of layered solvent knitting hyper-cross-linked microporous polymers (SHCPs) prepared by improving Friedel-Crafts reaction and using dichloroalkane as an economical solvent, stable electrophilic reagent, and external cross-linker at low temperature, which could be used as layered counterparts to obtain previously unknown 2D SHCP nanosheets by method of ultrasonic-assisted solvent exfoliation. This efficient and low-cost strategy can produce previously unreported microporous organic polymers with layered structure and high surface area and gas storage capacity. The pore structure and surface area of these polymers can be controlled by tuning the chain length of the solvent, the molar ratio of AlCl(3), and the size of monomers. Furthermore, we successfully obtain an unprecedentedly high–surface area HCP material (3002 m(2) g(−1)), which shows decent gas storage capacity (4.82 mmol g(−1) at 273 K and 1.00 bar for CO(2); 12.40 mmol g(−1) at 77.3 K and 1.13 bar for H(2)). This finding provides an opportunity for breaking the constraint of former knitting methods and opening up avenues for the design and synthesis of previously unknown layered HCP materials

Kirigami: large convolutional kernels improve deep learning-based RNA secondary structure prediction

We introduce a novel fully convolutional neural network (FCN) architecture for predicting the secondary structure of ribonucleic acid (RNA) molecules. Interpreting RNA structures as weighted graphs, we employ deep learning to estimate the probability of base pairing between nucleotide residues. Unique to our model are its massive 11-pixel kernels, which we argue provide a distinct advantage for FCNs on the specialized domain of RNA secondary structures. On a widely adopted, standardized test set comprised of 1,305 molecules, the accuracy of our method exceeds that of current state-of-the-art (SOTA) secondary structure prediction software, achieving a Matthews Correlation Coefficient (MCC) over 11-40% higher than that of other leading methods on overall structures and 58-400% higher on pseudoknots specifically.Comment: -Updated authorship and acknowledgement

TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition

One persistent challenge in Speech Emotion Recognition (SER) is the ubiquitous environmental noise, which frequently results in deteriorating SER performance in practice. In this paper, we introduce a Two-level Refinement Network, dubbed TRNet, to address this challenge. Specifically, a pre-trained speech enhancement module is employed for front-end noise reduction and noise level estimation. Later, we utilize clean speech spectrograms and their corresponding deep representations as reference signals to refine the spectrogram distortion and representation shift of enhanced speech during model training. Experimental results validate that the proposed TRNet substantially promotes the robustness of the proposed system in both matched and unmatched noisy environments, without compromising its performance in noise-free environments.14 pages, 3 figure

The Role of Proinflammatory Pathways in the Pathogenesis of Colitis-Associated Colorectal Cancer

Patients with inflammatory bowel disease (IBD) are at an increased risk of developing colorectal cancer (CRC). The risk factors of CRC in IBD patients include long disease duration, extensive colitis, severe histological inflammation, and coexistence with primary sclerosing cholangitis (PSC). Several molecular pathways that contribute to sporadic CRC are also involved in the pathogenesis of colitis-associated CRC. It is well established that long-standing chronic inflammation is a key predisposing factor of CRC in IBD. Proinflammatory pathways, including nuclear factor kappa B (NF-κB), IL-6/STAT3, cyclooxygenase-2 (COX-2)/PGE2, and IL-23/Th17, promote tumorigenesis by inducing the production of inflammatory mediators, upregulating the expression of antiapoptotic genes, and stimulating cell proliferation as well as angiogenesis. Better understanding of the underlying mechanisms may provide some promising targets for prevention and therapy. This review aims to elucidate the role of these signaling pathways in the pathogenesis of colitis-associated CRC using evidence-based approaches

Does epigenetic polymorphism contribute to phenotypic variances in Jatropha curcas L.?

<p>Abstract</p> <p>Background</p> <p>There is a growing interest in <it>Jatropha curcas </it>L. (jatropha) as a biodiesel feedstock plant. Variations in its morphology and seed productivity have been well documented. However, there is the lack of systematic comparative evaluation of distinct collections under same climate and agronomic practices. With the several reports on low genetic diversity in jatropha collections, there is uncertainty on genetic contribution to jatropha morphology.</p> <p>Result</p> <p>In this study, five populations of jatropha plants collected from China (CN), Indonesia (MD), Suriname (SU), Tanzania (AF) and India (TN) were planted in one farm under the same agronomic practices. Their agronomic traits (branching pattern, height, diameter of canopy, time to first flowering, dormancy, accumulated seed yield and oil content) were observed and tracked for two years. Significant variations were found for all the agronomic traits studied. Genetic diversity and epigenetic diversity were evaluated using florescence Amplified Fragment Length Polymorphism (fAFLP) and methylation sensitive florescence AFLP (MfAFLP) methods. Very low level of genetic diversity was detected (polymorphic band <0.1%) within and among populations. In contrast, intermediate but significant epigenetic diversity was detected (25.3% of bands were polymorphic) within and among populations. More than half of CCGG sites surveyed by MfAFLP were methylated with significant difference in inner cytosine and double cytosine methylation among populations. Principal coordinates analysis (PCoA) based on Nei's epigenetic distance showed Tanzania/India group distinct from China/Indonesia/Suriname group. Inheritance of epigenetic markers was assessed in one F1 hybrid population between two morphologically distinct parent plants and one selfed population. 30 out of 39 polymorphic markers (77%) were found heritable and followed Mendelian segregation. One epiallele was further confirmed by bisulphite sequencing of its corresponding genomic region.</p> <p>Conclusion</p> <p>Our study confirmed climate and practice independent differences in agronomic performance among jatropha collections. Such agronomic trait variations, however, were matched by very low genetic diversity and medium level but significant epigenetic diversity. Significant difference in inner cytosine and double cytosine methylation at CCGG sites was also found among populations. Most epigenetic differential markers can be inherited as epialleles following Mendelian segregation. These results suggest possible involvement of epigenetics in jatropha development.</p

Preparation of ZrB2-ZrC-SiC-ZrO2 nanopowders with in-situ grown homogeneously dispersed SiC nanowires

To explore the application of SiC nanowires (SiCnws) in ZrB2 based ceramic materials, a facile approach is reported to in situ synthesize homogeneously dispersed SiCnws in ZrB2-ZrC-SiC-ZrO2 nanopowders by pyrolyzing a B-Si-Zr containing sol precursor impregnated in polyurethane sponge. The sponge was used to provide porous skeletons for the growth of SiC nanowires and facilitate their uniform distribution in the powders. After heat-treatment of the precursor with a Si/Zr atomic ratio of 10 at 1500 °C for 2 h, ZrB2-ZrC-SiC-ZrO2 ceramic powders were obtained with an even and fine particle size of ~100 nm. The SiCnws were in a diameter of ~100 nm with a controllable length varying from tens to hundreds of microns by increasing the silicon content in the precursor. Moreover, the produced SiCnws were in high purity, and homogeneously dispersed in the hybrid nanopowders. The study can open up a feasible route to overcome the critical fabrication process in SiCnws reinforced ceramic matrix composites