Design and Fabrication of High Capacity Lithium-Ion Batteries using Electro-Spun Graphene Modified Vanadium Pentoxide Cathodes

Indiana University-Purdue University Indianapolis (IUPUI)Electrospinning has gained immense interests in recent years due to its potential application in various fields, including energy storage application. The V2O5/GO as a layered crystal structure has been demonstrated to fabricate nanofibers with diameters within a range of ~300nm through electrospinning technique. The porous, hollow, and interconnected nanostructures were produced by electrospinning formed by polymers such as Polyvinylpyrrolidone (PVP) and Polyvinyl alcohol (PVA), separately, as solvent polymers with electrospinning technique. In this study, we investigated the synthesis of a graphene-modified nanostructured V2O5 through modified sol-gel method and electrospinning of V2O5/GO hybrid. Electrochemical characterization was performed by utilizing Arbin Battery cycler, Field Emission Scanning Electron Microscopy (FESEM), X-ray powder diffraction (XRD), Thermogravimetric analysis (TGA), Mercury Porosimetry, and BET surface area measurement. As compared to the other conventional fabrication methods, our optimized sol-gel method, followed by the electrospinning of the cathode material achieved a high initial capacity of 342 mAh/g at a high current density of 0.5C (171 mA/g) and the capacity retention of 80% after 20 cycles. Also, the prepared sol-gel method outperforms the pure V2O5 cathode material, by obtaining the capacity almost two times higher. The results of this study showed that post-synthesis treatment of cathode material plays a prominent role in electrochemical performance of the nanostructured vanadium oxides. By controlling the annealing and drying steps, and time, a small amount of pyrolysis carbon can be retained, which improves the conductivity of the V2O5 nanorods. Also, controlled post-synthesis helped us to prevent aggregation of electro-spun twisted nanostructured fibers which deteriorates the lithium diffusion process during charge/discharge of batteries

A tiny public key scheme based on Niederreiter Cryptosystem

Due to the weakness of public key cryptosystems encounter of quantum computers, the need to provide a solution was emerged. The McEliece cryptosystem and its security equivalent, the Niederreiter cryptosystem, which are based on Goppa codes, are one of the solutions, but they are not practical due to their long key length. Several prior attempts to decrease the length of the public key in code-based cryptosystems involved substituting the Goppa code family with other code families. However, these efforts ultimately proved to be insecure. In 2016, the National Institute of Standards and Technology (NIST) called for proposals from around the world to standardize post-quantum cryptography (PQC) schemes to solve this issue. After receiving of various proposals in this field, the Classic McEliece cryptosystem, as well as the Hamming Quasi-Cyclic (HQC) and Bit Flipping Key Encapsulation (BIKE), chosen as code-based encryption category cryptosystems that successfully progressed to the final stage. This article proposes a method for developing a code-based public key cryptography scheme that is both simple and implementable. The proposed scheme has a much shorter public key length compared to the NIST finalist cryptosystems. The key length for the primary parameters of the McEliece cryptosystem (n=1024, k=524, t=50) ranges from 18 to 500 bits. The security of this system is at least as strong as the security of the Niederreiter cryptosystem. The proposed structure is based on the Niederreiter cryptosystem which exhibits a set of highly advantageous properties that make it a suitable candidate for implementation in all extant systems

New Automatic search method for Truncated-differential characteristics: Application to Midori, SKINNY and CRAFT

In this paper, using Mixed Integer Linear Programming, a new automatic search tool for truncated differential characteristic is presented. Our method models the problem of finding a maximal probability truncated differential characteristic, which is able to distinguish the cipher from a pseudo random permutation. Using this method, we analyse Midori64, SKINNY64/X and CRAFT block ciphers, for all of which the existing results are improved. In all cases, the truncated differential characteristic is much more efficient than the (upper bound of) bit-wise differential characteristic proven by the designers, for any number of rounds. More specifically, the highest possible rounds, for which an efficient differential characteristic can exist for Midori64, SKINNY64/X and CRAFT are 6, 7 and 10 rounds respectively, for which differential characteristics with maximum probabilities of $2^{-60}$, $2^{-52}$ and $2^{-62.61}$ (may) exist. Using our new method, we introduce new truncated differential characteristics for these ciphers with respective probabilities $2^{-54}$, $2^{-4}$ and $2^{-24}$ at the same number of rounds. Moreover, the longest truncated differential characteristics found for SKINNY64/X and CRAFT have 10 and 12 rounds, respectively. This method can be used as a new tool for differential analysis of SPN block ciphers

Histologic Evaluation of the Effects of Folinic Acid Chitosan Hydrogel and Botulinum Toxin A on Wound Repair of Cleft Lip Surgery in Rats

: The aim of the present study was to compare the effects of folinic acid chitosan hydrogel and botulinum toxin A on the wound repair of cleft lip surgery in rat animal models. Cleft lip defects were simulated by triangular incisions in the upper lip of 40 Wistar rats. Then, the rats were randomly assigned to four groups: control (CTRL), chitosan hydrogel (CHIT), and folinic acid chitosan hydrogel (FOLCHIT), in which the wounds were covered by a gauze pad soaked in normal saline, chitosan hydrogel, and folinic acid chitosan hydrogel, respectively for 5 min immediately after closure; and botulinum toxin A (BOT) with the injection of 3 units of botulinum toxin A in the wound region. Fibroblast proliferation, collagen deposition, inflammatory cell infiltration, neovascularization, and epithelial proliferation and each parameter were rated on days 14 and 28. Statistical analysis was performed by Kolmogorov-Smirnov test, Shapiro-Wilk test, Kruskal-Wallis, and post-hoc tests (α = 0.05). The mean score for fibroblast proliferation was significantly higher in the FOLCHIT group compared with the BOT group at days 14 and 28 (p < 0.001, p = 0.012, respectively). At day 28, collagen deposition was significantly higher in the FOLCHIT group compared with the BOT group (p = 0.012). No significant difference was observed between the inflammatory infiltration of the study groups at the two time points (p = 0.096 and p = 1.000, respectively). At day 14, vascular proliferation of group FOLCHIT was significantly higher than groups CTRL and CHIT (p = 0.001 and p = 0.006, respectively). The epithelial proliferation in the FOLCHIT group was significantly higher than groups CHIT and CTRL at day 14 (p = 0.006 and p = 0.001, respectively) and day 28 (p = 0.012). In simulated lip cleft defects, topical application of folinic acid induces faster initial regeneration by higher inflammation and cellular proliferation, at the expense of a higher tendency for scar formation by slightly higher fibroblast proliferation and collagen deposition. While injection of botulinum toxin A provides less fibroblast proliferation and collagen deposition, and thus lower potential for scar formation compared with the folinic acid group. Therefore, in wounds of the esthetic zone, such as cleft lip defects, the application of botulinum toxin A shows promising results

Dysregulated levels of glycogen synthase kinase-3β (GSK-3β) and miR-135 in peripheral blood samples of cases with nephrotic syndrome

Background. Glycogen synthase kinase-3 (GSK-3β) is a serine/threonine kinase with multifunctions in various physiological procedures. Aberrant level of GSK-3β in kidney cells has a harmful role in podocyte injury. Methods. In this article, the expression levels of GSK-3β and one of its upstream regulators, miR-135a-5p, were measured in peripheral blood mononuclear cells (PBMCs) of cases with the most common types of nephrotic syndrome (NS); focal segmental glomerulosclerosis (FSGS) and membranous glomerulonephritis (MGN). In so doing, fifty-two cases along with twenty-four healthy controls were included based on the strict criteria. Results. Levels of GSK-3β mRNA and miR-135 were measured with quantitative realtime PCR. There were statistically significant increases in GSK-3β expression level in NS (P = 0.001), MGN (P = 0.002), and FSGS (P = 0.015) groups compared to the control group. Dysregulated levels of miR-135a-5p in PBMCs was not significant between the studied groups. Moreover, a significant decrease was observed in the expression level of miR-135a-5p in the plasma of patients with NS (P = 0.020), MGN (P = 0.040), and FSGS (P = 0.046) compared to the control group. ROC curve analysis approved a diagnostic power of GSK-3β in discriminating patients from healthy controls (AUC: 0.72, P = 0.002) with high sensitivity and specificity. Conclusions. Dysregulated levels of GSK-3β and its regulator miR-135a may participate in the pathogenesis of NS with different etiology. Therefore, more research is needed for understanding the relationship between them

Anti-fibrotic effects of curcumin and some of its analogues in the heart.

Cardiac fibrosis stems from the changes in the expression of fibrotic genes in cardiac fibroblasts (CFs) in response to the tissue damage induced by various cardiovascular diseases (CVDs) leading to their transformation into active myofibroblasts, which produce high amounts of extracellular matrix (ECM) proteins leading, in turn, to excessive deposition of ECM in cardiac tissue. The excessive accumulation of ECM elements causes heart stiffness, tissue scarring, electrical conduction disruption and finally cardiac dysfunction and heart failure. Curcumin (Cur; also known as diferuloylmethane) is a polyphenol compound extracted from rhizomes of Curcuma longa with an influence on an extensive spectrum of biological phenomena including cell proliferation, differentiation, inflammation, pathogenesis, chemoprevention, apoptosis, angiogenesis and cardiac pathological changes. Cumulative evidence has suggested a beneficial role for Cur in improving disrupted cardiac function developed by cardiac fibrosis by establishing a balance between degradation and synthesis of ECM components. There are various molecular mechanisms contributing to the development of cardiac fibrosis. We presented a review of Cur effects on cardiac fibrosis and the discovered underlying mechanisms by them Cur interact to establish its cardio-protective effects

Design and Fabrication of High Capacity Lithium-Ion Batteries using Electro-Spun Graphene Modified Vanadium Pentoxide Cathodes

Indiana University-Purdue University Indianapolis (IUPUI)Electrospinning has gained immense interests in recent years due to its potential application in various fields, including energy storage application. The V2O5/GO as a layered crystal structure has been demonstrated to fabricate nanofibers with diameters within a range of ~300nm through electrospinning technique. The porous, hollow, and interconnected nanostructures were produced by electrospinning formed by polymers such as Polyvinylpyrrolidone (PVP) and Polyvinyl alcohol (PVA), separately, as solvent polymers with electrospinning technique. In this study, we investigated the synthesis of a graphene-modified nanostructured V2O5 through modified sol-gel method and electrospinning of V2O5/GO hybrid. Electrochemical characterization was performed by utilizing Arbin Battery cycler, Field Emission Scanning Electron Microscopy (FESEM), X-ray powder diffraction (XRD), Thermogravimetric analysis (TGA), Mercury Porosimetry, and BET surface area measurement. As compared to the other conventional fabrication methods, our optimized sol-gel method, followed by the electrospinning of the cathode material achieved a high initial capacity of 342 mAh/g at a high current density of 0.5C (171 mA/g) and the capacity retention of 80% after 20 cycles. Also, the prepared sol-gel method outperforms the pure V2O5 cathode material, by obtaining the capacity almost two times higher. The results of this study showed that post-synthesis treatment of cathode material plays a prominent role in electrochemical performance of the nanostructured vanadium oxides. By controlling the annealing and drying steps, and time, a small amount of pyrolysis carbon can be retained, which improves the conductivity of the V2O5 nanorods. Also, controlled post-synthesis helped us to prevent aggregation of electro-spun twisted nanostructured fibers which deteriorates the lithium diffusion process during charge/discharge of batteries

Handling Novel and Out-Of-Distribution Data in Deep Learning : OOD Detection and Shortcut Mitigation

Advancements in machine learning, and particularly deep learning, have revolutionized the real-world applications of artificial intelligence in recent years. A main property of deep neural models is their ability to learn a task based on a set of examples, that is, the training data. Although the state-of-the-art performance of such models is promising in many tasks, this of-ten holds only as long as the inputs to the model are “sufficiently similar” to the training data. Mathematically, a ubiquitous assumption in machine learning studies is that the test data used for evaluating a model are sampled from the same probability distribution as the training data. It is challenging to approach any problem where this assumption is violated, as it requires handling Out-Of-Distribution (OOD) data, i.e., data points that are systematically different from the training (in-distribution) data. In particular, one might be interested in detecting OOD inputs at test time given an unlabeled training set, which is the main problem explored in this thesis. This type of OOD detection (a.k.a. novelty/anomaly detection) has various applications in discovering unusual events and phenomena as well as improving safety in AI systems. Another challenging problem in deep learning is that a model might rely on certain trivial relations (spurious correlations) existing in training data to solve a task. Such “shortcuts” can bring a high performance on in-distribution data, but they may collapse on more realistic OOD data. It is therefore vital to mitigate the shortcut learning effects in deep models, which is the second topic studied in this thesis. A part of the present thesis is concerned with leveraging pretrained deep models for OOD detection on images, without modifying their standard training algorithms. A method is proposed to use invertible (flow-based) generative models based on null hypothesis testing ideas, leading to an OOD detection method that is fast and more reliable than the traditional likelihood-based method. Diffusion (score-based) models are another type of modern generative models used for OOD detection in this thesis, in combination with pretrained deep encoders. Another contribution of the thesis is in leveraging the power of large self-supervised models in fully unsupervised fine-grained OOD detection. It is shown that the simple k-nearest neighbor distance in the representation space of such models results in a reasonable performance but can be boosted substantially through the proposed adjustments, without any model fine-tuning. The local geometry of representations and background (irrelevant) features are considered to this end. OOD detection with time series data is another problem studied in this thesis. Specifically, a method is proposed based on Contrastive Predictive Coding (CPC) self-supervised learning, and applied to detect novel categories in human activity data. It is demonstrated, both empirically and through theoretical motivation, that modifying the CPC to use a radial basis function instead of the conventional log-bilinear function is a requirement for reliable and efficient OOD detection. This extension is combined with quantization of representation vectors to achieve better performance. This thesis also addresses the problem of learning deep representations (transfer learning) in a situation where a shortcut exists in data. In this problem, a deep model is trained on a shortcut-biased image dataset to solve a self-supervised or supervised classification task. The representations learned by this model are used to train a smaller model on a related but different downstream task, and the adverse effect of the shortcut is verified empirically there. Moreover, a method is proposed to enhance the representation learning in this scenario, based on an auxiliary model trained in an adversarial manner along with the upstream classifier

New Automatic Search Method for Truncated-Differential Characteristics Application to Midori, SKINNY and CRAFT

Abstract In this paper, using Mixed-Integer Linear Programming, a new automatic search tool for truncated differential characteristic is presented. Our method models the problem of finding a maximal probability truncated differential characteristic, being able to distinguish the cipher from a pseudo-random permutation. Using this method, we analyze Midori64, SKINNY64/X and CRAFT block ciphers, for all of which the existing results are improved. In all cases, the truncated differential characteristic is much more efficient than the (upper bound of) bit-wise differential characteristic proven by the designers, for any number of rounds. More specifically, the highest possible rounds, for which an efficient differential characteristic can exist for Midori64, SKINNY64/X and CRAFT are 6, 7 and 10 rounds, respectively, for which differential characteristics with maximum probabilities of $2^{-60}$, $2^{-52}$ and $2^{-62.61}$ (may) exist. Using our new method, we introduce new truncated differential characteristics for these ciphers with respective probabilities $2^{-54}$, $2^{-4}$ and $2^{-24}$ at the same number of rounds. Moreover, the longest truncated differential characteristics found for SKINNY64/X and CRAFT have 10 and 12 rounds, respectively. This method can be used as a new tool for differential analysis of SPN block ciphers.</jats:p

Handling Novel and Out-Of-Distribution Data in Deep Learning [Elektronisk resurs] : OOD Detection and Shortcut Mitigation

Advancements in machine learning, and particularly deep learning, have revolutionized the real-world applications of artificial intelligence in recent years. A main property of deep neural models is their ability to learn a task based on a set of examples, that is, the training data. Although the state-of-the-art performance of such models is promising in many tasks, this of-ten holds only as long as the inputs to the model are “sufficiently similar” to the training data. Mathematically, a ubiquitous assumption in machine learning studies is that the test data used for evaluating a model are sampled from the same probability distribution as the training data. It is challenging to approach any problem where this assumption is violated, as it requires handling Out-Of-Distribution (OOD) data, i.e., data points that are systematically different from the training (in-distribution) data. In particular, one might be interested in detecting OOD inputs at test time given an unlabeled training set, which is the main problem explored in this thesis. This type of OOD detection (a.k.a. novelty/anomaly detection) has various applications in discovering unusual events and phenomena as well as improving safety in AI systems. Another challenging problem in deep learning is that a model might rely on certain trivial relations (spurious correlations) existing in training data to solve a task. Such “shortcuts” can bring a high performance on in-distribution data, but they may collapse on more realistic OOD data. It is therefore vital to mitigate the shortcut learning effects in deep models, which is the second topic studied in this thesis.A part of the present thesis is concerned with leveraging pretrained deep models for OOD detection on images, without modifying their standard training algorithms. A method is proposed to use invertible (flow-based) generative models based on null hypothesis testing ideas, leading to an OOD detection method that is fast and more reliable than the traditional likelihood-based method. Diffusion (score-based) models are another type of modern generative models used for OOD detection in this thesis, in combination with pretrained deep encoders. Another contribution of the thesis is in leveraging the power of large self-supervised models in fully unsupervised fine-grained OOD detection. It is shown that the simple k-nearest neighbor distance in the representation space of such models results in a reasonable performance but can be boosted substantially through the proposed adjustments, without any model fine-tuning. The local geometry of representations and background (irrelevant) features are considered to this end.OOD detection with time series data is another problem studied in this thesis. Specifically, a method is proposed based on Contrastive Predictive Coding (CPC) self-supervised learning, and applied to detect novel categories in human activity data. It is demonstrated, both empirically and through theoretical motivation, that modifying the CPC to use a radial basis function instead of the conventional log-bilinear function is a requirement for reliable and efficient OOD detection. This extension is combined with quantization of representation vectors to achieve better performance.This thesis also addresses the problem of learning deep representations (transfer learning) in a situation where a shortcut exists in data. In this problem, a deep model is trained on a shortcut-biased image dataset to solve a self-supervised or supervised classification task. The representations learned by this model are used to train a smaller model on a related but different downstream task, and the adverse effect of the shortcut is verified empirically there. Moreover, a method is proposed to enhance the representation learning in this scenario, based on an auxiliary model trained in an adversarial manner along with the upstream classifier.</p