Miracle-dz/UniFAR-FTUs huggingface dataset:
Fabrication of Gelatin-based natural nanocomposite films using nanoclay and Chitosan for food packaging applications

* Corresponding Author Email: mmzerafat@shirazu.ac. ir How to cite this article Moosavi SMR, Zerafat MM. Fabrication of Gelatin-based natural nanocomposite films using nanoclay and Chitosan for food packaging applications. Int. J. Nano Dimens. , 2021; 12(4): 343-354. Abstract Solid waste disposal and accumulation from various packaging materials made of synthetic polymers have led to intensified environmental concerns during the last decades. However, extensive research has been conducted on bio-nanocomposites as an alternative material for synthetic polymers in packaging applications during recent years. In this study, gelatin-based films modified with nano-caly and chitosan are proposed for food packaging. Nano-clay is used to improve various features such as mechanical strength and water vapor barrier properties. Also, chitosan is added to the gelatin matrix for anti-microbial and anti-fungal properties. The as-prepared nanocomposite films were characterized using FTIR, XRD and SEM techniques. Also, tensile strength, transparency, swelling behavior, water contact angle and permeability tests were performed. Based on the results, increasing nano-clay up to 5 wt. %, results in 25% tensile strength enhancement in the nano-composite film. Higher nano-clay contents led to strength decline. Elongation at break is reduced by increasing the nano-clay content from 25% to 40%. The contact angle was also reduced by increasing the nano-clay content from 95o to 90o showing a small hydrophobicity reduction.
Sea grapes extract improves blood glucose, total cholesterol, and PGC-1α in rats fed on cholesterol- and fat-enriched diet

Background: Sea grapes or Caulerpa racemosa have a lot of phytochemical content, especially unsaturated fatty acids that are beneficial for health. This study aims to evaluate the effects of sea grapes extract on blood glucose levels, total cholesterol-, and Peroxisome proliferator-activated receptor-gamma coactivator (PGC)-1α in male Wistar rats, which were given per-oral (p.o.) cholesterol- and carbohydrates fat-enriched diets (CFED). Methods: Forty male Wistar albino rats weighing between 200 – 250 g were used for this study. Animals were randomly distributed into four groups of ten animals each. Group A served as control (received standard dry pellet diet). Rats in group B were fed on CFED for 4 weeks. Groups C and D were fed on CFED and were administered 150 and 450 mg/kg of sea grapes extract (p.o.), respectively. Results: Group C rats indicated a blood glucose reduction and an increase in PGC-1α serum, in comparison to group D (p<0.05). There were no significant differences between group C and D in blood cholesterol reduction (high dose of the extract did not have significant effects) (p=0.222), and both groups had the same effect in lowering total cholesterol in rats. Conclusion: Sea grapes extract is proven to improve blood glucose, total cholesterol, and PGC-1α levels in rats fed with CFED.
Neural Axiom Network for Knowledge Graph Reasoning

Knowledge graphs (KGs) generally suffer from incompleteness and incorrectness problems due to the automatic and semi-automatic construction process. Knowledge graph reasoning aims to infer new knowledge or detect noises, which is es-sential for improving the quality of knowledge graphs. In recent years, various KG reasoning techniques, such as symbolic- and embedding-based methods, have been proposed and shown strong reasoning ability. Symbolic-based reasoning methods infer missing triples according to predeﬁned rules or ontologies. Although rules and axioms have proven to be effective, it is difﬁcult to obtain them. While embedding-based reasoning methods represent entities and relations of a KG as vectors, and complete the KG via vector computation. However, they mainly rely on structural information, and ignore implicit axiom information that are not predeﬁned in KGs but can be reﬂected from data. That is, each correct triple is also a logically consistent triple, and satisﬁes all axioms. In this paper, we propose a novel NeuR al A xiom N etwork ( NeuRAN ) framework that combines explicit structural and implicit axiom information. It only uses existing triples in KGs without introducing additional ontologies. Speciﬁcally, the framework consists of a knowledge graph embedding module that preserves the semantics of triples, and ﬁve axiom modules that encode ﬁve kinds of implicit axioms using entities and relations in triples. These axioms correspond to ﬁve typical object property expression axioms deﬁned in OWL2, including ObjectPropertyDomain, ObjectPropertyRange, DisjointObjectProperties, IrreﬂexiveObjectProperty and AsymmetricObjectProperty . The knowledge graph embedding module and axiom modules respectively compute the scores that the triple conforms to the semantics and the corresponding axioms. Evaluations on KG reasoning tasks show the efﬁciency of our method. Compared with knowledge graph embedding models and CKRL, our method achieves comparable performance on noise detection and triple classiﬁcation, and achieves signiﬁcant performance on link prediction. Compared with TransE and TransH, our method improves the link prediction performance on the Hit@1 metric by 22.4% and 21.2% on WN18RR-10% dataset respectively.
Evolution of a Compiling Query Engine

In 2011 we showed how to use dynamic code generation to process queries in a data-centric manner. This execution model can produce compact and efficient code and was successfully used by both our own systems and systems of other groups. As the systems become used in practice, additional techniques were developed for shortcomings that did arrive, including low-latency compilation, multi-threading support, and others. This paper gives an overview of the evolution of our query engine within in the last ten years, and points out which problem have to be tackled to bring a compiling system into production usage.
Global optimization for non-convex programs via convex proximal point method

In this study, a convex proximal point algorithm (CPPA) is considered for solving constrained non-convex problems, and new theoretical results are proposed. It is proved that every cluster point of CPPA is a stationary point, and the initial point of CPPA is key to global optimization. Several sufficient conditions for the initial point selection are provided for CPPA to find the global minimum. Motivated by these results, numerical experiments were conducted on non-convex quadratic programming problems with convex quadratic constraints. The performance of CPPAs was compared, with the initial point randomly selected or obtained through the Lagrangian dual problem. The numerical results demonstrate that the quality of the CPPA with the computed Lagrangian dual initial point is much better than that with the random initial point, in terms of the objective function value.
Function-as-a-Service: From An Application Developer’s Perspective

In the past few years, FaaS has gained signiﬁcant popularity and became a go-to choice for deploying cloud applications and micro-services. FaaS with its unique ‘pay as you go’ pricing model and key performance beneﬁts over other cloud services, o ﬀ ers an easy and intuitive programming model to build cloud applications. In this model, a developer focuses on writing the code of the application while infrastructure management is left to the cloud provider who is responsible for the underlying resources, security, isolation, and scaling of the application. Recently, a number of commercial and open-source FaaS platforms have emerged, o ﬀ ering a wide range of features to application developers. In this paper, ﬁrst, we present measurement studies demystifying various features and performance of commercial and open-source FaaS platforms that can help developers with deploying and con-ﬁguring their serverless applications. Second, we discuss the distinct performance and cost beneﬁts of FaaS and interesting use cases that leverage the performance, cost, or both aspects of FaaS. Lastly, we discuss challenges a developer may face while developing or deploying a serverless application. We also discuss state of the art solutions and open problems.
"Prevention through Deterrence" Against Citizens: the Venezuela-Colombia Border during the COVID-19 Pandemic and Human Rights Implications

NEGRETTI BENITO, ANDREINA "Prevention through Deterrence" Against Citizens: the Venezuela-Colombia Border during the COVID-19 Pandemic and Human Rights Implications ADVISOR: LEO ZAIBERT This thesis analyses the human rights implications of the measures taken by the Venezuelan government at the Venezuelan-Colombian border during the COVID-19 pandemic. I will argue that the goal of these measures is preventing or impeding the return of citizens through "deterrence techniques" that have been historically used by other countries. This case's importance relies on the fact that, unlike other cases, the Venezuelan government uses these "techniques" against its own nationals, rather than against unwanted immigrants. The first chapter will provide an overview of the theoretical framework concerning migration, arguments regarding open borders, and human rights protections. This will allow me to consider internationally established definitions, principles, and agreements, both in customary and conventional law, in the analysis of the Venezuelan case. The second chapter will describe the pre-existent waves of emigration in Venezuela, the current situation, and the international response to the on-going crisis, which will explain underlying causes of current migration movements. The third chapter will discuss the impact of COVID-19 in Colombia and Venezuela and the measures taken by each government, including a comparison between the declared states of emergency. Different factors like access to health services, working conditions, xenophobia, and discrimination, will be an important component as I highlight the erosion of migrants’ rights and raise concerns regarding potential human rights violations. In the fourth chapter I will examine the Venezuelan response to returnees and compare Maduro's government's questionable measures to traditional “prevention through deterrence” strategies, highlighting the deleterious human rights effects of the former.
Perioperative Anesthetics and Adjuvant Anxiolytic Approaches for Cataract Surgery Patients: Exploring Options

Cataract surgery is one of the most common surgeries performed worldwide. It has high rates of success and minimal adverse outcomes. In spite of the surgery’s effectiveness, patients frequently report perioperative anxiety. This anxiety is often treated with pharmacologic sedation; however, evidence suggests that this is unnecessary and can lead to poorer patient outcomes. There is evidence that other adjuvant therapies exist which can decrease perioperative anxiety and therefore the need for higher levels of pharmacologic sedation. This paper explores some of these adjuvant therapies, as well as current anesthetic technique used in cataract surgery.
Active two-phase learning for classification of large datasets with extreme class-skew

Active learning is a commonly used technique to reduce the amount of labeled data necessary for supervised learning. In this paper, we focus on collection of labeled examples in a domain with large unlabeled dataset and extreme class imbalance. This scenario presents several challenges to Active learning. Traditional active learning strategies can face acute difficulty in locating minority class examples and can fail completely due to the well-known cold start problem. The problem is further complicated by scale as for large datasets it can be expensive to execute Active learning computations on the whole domain set. Additionally, the active learning strategies can turn out to be impractical or inefficient for interactive use due to high computation time for the iterative selection cycles. In this paper, we proposed a two-phase approach that takes into account both high-class imbalance and the scale of the input data space. Specifically, our approach employs two active learners in a tiered fashion - first phase active learner efficiently learns a domain classifier (a filter function) defined on the entire input space and second learner tries to efficiently learn final ML classifier defined on the output of filter. The second-phase selects informative instances from a smaller pool of unlabeled examples which doesn’t require operating on the full dataset. The proposed method allows active learning to be applied to large datasets with class skew. The two-phases are interleaved (rather than isolated) and allow for a bi-directional flow of information. The combined two-phase learner progressively expands knowledge of input data space and uses successive first phase and second phase strategies to switch between learning the decision boundary and expanding domain boundary. Given labeled data at certain iteration, the second-phase focuses on exploiting the decision boundary (up to a performance threshold) and then, first-phase focuses on exploiting given information to intelligently search and expand the domain. We demonstrate the effectiveness of our strategy for product classification on sample of Amazon catalog dataset. Our results show that the proposed
TASK 1B DCASE 2021: AUDIO-VISUAL SCENE CLASSIFICATION WITH SQUEEZE-EXCITATION CONVOLUTIONAL RECURRENT NEURAL NETWORKS

Automatic scene classiﬁcation has always been one of the core tasks in every edition of the DCASE challenge. Until this edition, such classiﬁcation was performed using only audio data, and so the problematic was deﬁned as Acoustic Scene Classiﬁcation (ASC). In this 2021 edition, audio data is accompanied with visual data, providing additional information that can be jointly exploited for achieving higher recognition accuracy. The proposed approach makes use of two separate networks which are respectively trained in isolation on audio and visual data, so that each network specializes in a given modality. After training each network, the fusion of information from the audio and visual subnetworks is performed at two different stages. The early fusion stage combines features resulting from the last convolutional block of the respective subnetworks at different time steps to feed a bidirectional recurrent structure. The late fusion stage combines the output of the early fusion stage with the independent predictions provided by the two subnetworks, resulting in the ﬁnal prediction. For the visual subnetwork, a VGG16 architecture pretrained on the Places365 dataset is used, applying a ﬁne-tuning strategy over the Challenge dataset. On the other hand, the audio subnetwork is trained from scratch and uses squeeze-excitation techniques as in previous contributions from this team. As a result, the ﬁnal accuracy of the system is 92% on development split, outperforming the baseline by 15 percentage points
Anti-rat ICAM-1 antibody does not influence the course of experimental melanin-induced uveitis

Purpose. Experimental melanin-induced uveitis (EMIU) is a T cell-mediated rat model of acute anterior uveitis. We investigated the possibility of preventing the inflammation with monoclonal antibody directed against rat intercellular adhesion molecule 1 (ICAM-1). Methods. To induce EMIU, Lewis rats were immunized with bovine ocular melanin extract (250–500 µg). Each day from day 6 post-immunization, rats were injected intraperitoneally with anti-ICAM-1 monoclonal antibody (IA29) or normal mouse serum, and examined with a slit-lamp biomicroscope. On the first day of clinical inflammation, intravital microscopy of iris vasculature was performed on each animal following intraperitoneal injection of rhodamine 6G. At the peak of clinical inflammation, rats were killed, and eyes were examined histologically. Binding potency of IA29 was tested by flow cytometry using concanavalin A-stimulated rat T cells. Immunohistochemical staining was used to detect IA29 on rat uveal vascular endothelium. Results. The ability of IA29 to bind T cell blasts was present to a 1:2000 dilution, and IA29 was readily detectable on uveal vascular endothelium following systemic administration. However, there was no significant difference (p > 0.05) in incidence, time of onset, or severity or histological appearance of EMIU for the rats treated with IA29 when compared with the control rats. Intravital microscopy revealed sticking of leukocytes in the iris vasculature in both groups. Conclusions. We were unable to demonstrate an inhibitory effect of anti-rat ICAM-1 antibody on the outcome of EMIU. Our observations may reflect a redundancy in the adhesion molecule profile responsible for this uveitis.
Droplets Manipulated on Photothermal Organogel Surfaces

A photoresponsive organogel surface (POS), which integrates characteristics of the photothermal property of Fe3O4 nanoparticles and the low hysteresis feature of lubricant‐infused organogels, is shown. A photothermally induced dynamic temperature gradient can be formed rapidly at the location of near‐infrared‐light irradiation (NIR) on POS with suitable Fe3O4 nanoparticles content. Thus, various droplets (e.g., water, glycerol, ethylene glycol, propylene glycol, and ethanol) can be transported effectively and nimbly (e.g., along desired trajectories with controllable velocity and direction, even run uphill and deliver solid particles). This work reveals a synergistic effect between the asymmetrical droplet deformation and the inside Marangoni flows, which forms a unique driving force for droplet transport with high efficiency. This finding offers insight into the design of novel responsive interface materials for droplet transportation, which would be significant for laboratory‐on‐a‐chip contexts, mass transportation, and microengines.
Preparation, Characterization and Photo Activity of Copper Oxide Doped Titania Nano Catalyst

Copper oxide doped titania nanocatalysts preparation was carried out by combustion synthesis method. The precursor compounds for the preparation of these nanocatalysts were titanium iso propoxide, glycine, copper nitrate and strontium nitrate. The prepared catalysts were characterized by Energy dispersive X-ray analysis (EDX), X-ray diffraction (XRD), Scanning electron microscopy (SEM), BET Surface area measurement and Infrared spectroscopy (FTIR). Total acidity of the prepared catalysts were determined by temperature programmed desorption of ammonia (TPD-NH3). There was no characteristic peak of copper oxide in the XRD pattern. The photo activity of these prepared catalysts was examined for the degradation of methylene blue dye, which is a common environmental pollutant. These catalysts can be used to degrade the dye from water and thus helps the aquatic life.
La microeconomÃ­a de la producciÃ³n y trÃ¡fico de cocaÃ­na en Colombia

Este capA­tulo presenta una radiografA­a detallada de la microeconomA­a de la producciA³n y trAifico de cocaA­na en Colombia. El capA­tulo presenta una breve descripciA³n de la evoluciA³n en el tiempo de las cifras agregadas sobre producciA³n de cocaA­na y luego seconcentra en describir en detalle cada eslabA³n de la cadena de producciA³n. En particular, se describen los principales costos e ingresos en cada eslabA³n y, con base en la informaciA³n disponible, se presenta una estimaciA³n de los flujos de recursos que semueven en la economA­a colombiana producto de estas actividades ilegales. Las fases de producciA³n del clorhidrato de cocaA­na comprenden desde procesos de economA­a campesina, que incluye transformaciones de bajo valor agregado, producciA³n a baja ymediana escala de base de coca, participaciA³n de actores armados ilegales en los eslabones de mayor generaciA³n de valor agregado, y complejas redes de distribuciA³n de precursores y control sobre las rutas del narcotrAifico. Nuestros estimativos indican que, con datos a 2008, el tamaA±o del negocio de producciA³n y trAifico de cocaA­na en Colombia es de aproximadamente $13.6 billones de pesos (2.3% del PIB), valor agregado que se distribuye asA­: $1.2 billones corresponden al valor de la hoja de coca producida; $0.8 billones al valor agregado de la base de coca; $2 billones al valor agregado de la transformaciA³n de base en clorhidrato de cocaA­na; y $9.6 billones alvalor generado en el eslabA³n del trAifico.
Chemical-diffusive models for flame acceleration and transition-to-detonation: genetic algorithm and optimisation procedure

This paper presents a general approach for developing an automated, fast and flexible procedure to determine the reaction parameters for a simplified chemical-diffusive model to simulate flame acceleration and deflagration-to-detonation transition (DDT) in a stoichiometric methane–air mixture. The procedure uses a combination of a genetic algorithm and Nelder-Mead optimisation scheme to find the optimal reaction parameters for a reaction rate based on an Arrhenius form for conversion of reactants to products. The model finds six optimal reaction parameters that reproduce six flame and detonation properties. Results show that the reaction parameters closely reproduce their intended flame and detonation properties. The laminar flame profile computed using the reaction parameters in a 1D Navier-Stokes code matches the profile obtained when using a detailed chemical reaction mechanism. The optimal reaction parameters are then used in a 2D simulation of flame acceleration and DDT in an obstacle-laden channel containing stoichiometric methane–air, and the results show that the computation closely follows the transition-to-detonation observed in experiments. This automated procedure for finding parameters for a proposed reaction model makes it possible to simulate the behaviour of flames and detonations in large, complex scenarios, which would otherwise be an incalculable problem.
Effect of Dietary Supplementation with Propylene Glycol on Blood Metabolites and Hormones of Nili-Ravi Buffalo Heifers: Effect of dietary supplementation on blood metabolites

The current study was designed to investigate the effects of propylene glycol (PG) on blood metabolites, feed intake and fertility in buffalo heifers. For this purpose, 12 Nili-Ravi buffalo heifers were selected and divided into three groups A, B and C. Group A was kept as control while group B was supplemented with 150 g of PG and group C was supplemented with 300 g propylene glycol per animal per day for 30 days. Blood samples were taken on day 0, then 1st, 3rd, 5th and 7th week after initiation of PG supplementation. The serum glucose, triglycerides, total protein, albumin, alanine transaminase, total cholesterol, blood urea nitrogen, oestrogen and progesterone were determined. Results revealed that serum glucose level significantly (P<0.05) increased in both treated group as compared to control group. Total protein, alanine aminotransferase (ALT), albumin and progesterone concentrations increased significantly (P<0.05) in treated groups as compared to control group. In treated groups serum cholesterol, blood urea nitrogen, and triglycerides are significantly decreased (P<0.05). Based on results of the present and previous studies, it was concluded that feed supplementation of propylene glycol on blood metabolites and reproductive hormones showed remarkable changes.