Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant and Anti-inflammatory Properties of Resveratrol in Diabetic Nephropathy: A Systematic Review and Meta-analysis of Animal Studies

Background: Accumulated experimental evidence suggests that resveratrol may have an effect on diabetic nephropathy by inhibiting inflammation and decreasing oxidative stress. However, the credibility of the evidence for this practice is unclear. Thus, we aimed to perform a systematic review and meta-analysis of animal studies to evaluate the antioxidant and anti-inflammatory properties of resveratrol when used in the treatment of diabetic nephropathy. Methods: Electronic bibliographic databases including PubMed, EMBASE, and Web of Science were searched for relevant studies. The methodological quality of animal studies was assessed based on the SYstematic Review Center for Laboratory animal Experimentation Risk of Bias (SYRCLE’s RoB) tool. A meta-analysis was performed based on the Cochrane Handbook for Systematic Reviews of Interventions by using RevMan 5.4 software. This study was registered within International Prospective Register of Systematic Reviews (PROSPERO) as number CRD42021293784. Results: Thirty-six qualified studies involving 726 animals were included. There was a significant association of resveratrol with the levels of blood glucose (BG), serum creatinine (Scr), blood urea nitrogen (BUN), catalase (CAT), superoxide dismutase (SOD), malondialdehyde (MDA), glutathione (GSH), glutathione peroxidase (GPx), and interleukin-1β (IL-1β). Nevertheless, resveratrol treatment did not effectively decrease the levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). In addition, more remarkable antioxidant and hypoglycemic effects were observed in type 2 diabetic nephropathy rather than in type 1 diabetic nephropathy based on subgroup analysis. Conclusion: In this meta-analysis, resveratrol can exert its antioxidant activities by reducing the levels of MDA and recovering the activities of SOD, CAT, GSH, and GPx. With regard to pro-inflammatory cytokines, resveratrol had a positive effect on the reduction of IL-1β. However, the analysis indicated that resveratrol had no effect on IL-6 and TNF-α levels, probably because of the methodological quality of the studies and their heterogeneity. Current evidence supports the antioxidant and anti-inflammatory properties of resveratrol, but its relationship with the levels of some inflammatory cytokines such as IL-6 and TNF-α in animals with diabetic nephropathy needs further elucidation.
StoryPass: a system and study for memorable secure passphrases.

The goal of this thesis is to study the implementation of a passphrase system that implements new creation policies, called StoryPass. We are motivated to do this research as current text-based authentication methods, such as the password, fail to provide adequate security and usability. We call our system StoryPass because we were inspired by previous research which states that information created with stories can be more memorable. The problem we address is the lack of research on secure and usable passphrase creation guidelines. Our main contributions include a theoretical security analysis, a controlled 39-day user study and an estimate of the security that the resulting passphrases provide. Our security estimates are mainly performed through an algorithm that uses n-grams to estimate the number of attempts required to successfully guess passphrases created in StoryPass. We were able to successfully guess 64% of the passphrases collected during our 39-day user study, but with only a very large number of attempts. The passphrases which were not guessed generally contained slang and “non-words” which are words that are not found in standard dictionaries. Using a sentence-like structure in passphrases greatly improved usability. Memory errors were the leading cause of failed login; error correction techniques were used to prevent login failures from typographical errors. This thesis discusses how results from our user study can be used to help guide future passphrase creation policies.
Simulation Study of DC De-icing Scheme for China Southern Power Grid

Based on the de-icing current calculation of Fu-Quan substation transmission lines,the 60 MW/25 MW DC de-icer is deployed to 500 kV/220 kV lines,and for 35 kV/10 kV lines a mobile DC de-icer is designed,which can be driven by 500 kW diesel power generator.The DC de-icing systems are studied with PSCAD/EMTDC to prove their feasibility and effectivity as well as the recommended specifications of the DC de-icers.
A Review On Anomaly Detection and Classification In Students’ Assessment Dataset

Anomaly detection is a problem that has been of great interest in diverse research fields, which is aimed at finding patterns or behaviours that do not conform to the expected one. Many anomaly detection techniques has be applied in detecting anomalies in various fields, such as in intrusion detection, computer networks, health etc. In this review paper, we looked at an overview of anomaly detection techniques and detecting anomalies in students’ assessment dataset. Thereafter we classified the reviewed papers/articles on anomaly detection in students’ assessment data and the use of SVM and K-NN classifiers in detecting anomalies. It was observed that very little has been done with regards detecting anomalies in students’ results/assessment scores. SVM is very good in detecting anomalies in datasets with high data instances and features with high sparsity. Most interesting areas of applications as reviewed in this paper are in intrusion detection, healthcare (medicine) and computer networks.
Unsupervised Multi-Label Document Classification for Large Taxonomies Using Word Embeddings

More and more businesses are in need for metadata for their documents. However, automatic generation for metadata is not easy, as for supervised document classification, a significant amount of labelled training data is needed, which is not always present in the desired amount or quality. Often, documents need to be tagged with a predefined set of company specific keyword that are organized in a taxonomy. We present an unsupervised approach to perform multi-label document classification for large taxonomies using word embeddings and evaluate it with a dataset of a public broadcaster under two assumptions – “perfect rationality” and “bounded rationality”, which allows the approach to outperform the average performance humans. We point out strengths of the approach compared to syntactical approaches like tf-idf and show that also allowing to match keywords on a higher taxonomy level can significantly increase precision and recall.
Comparative Study of Dual Material Asymmetric Double Gate Junctionless and Junction Transistor

In this paper, we present the features of asymmetric DMDG Junctionless transistor and compared it with a similar junction transistor. This comparison has been carried out using 2D models of the transistors. The parameters we have calculated are threshold voltage, drain induced barrier lowering (DIBL), Ion/ Ioff ratio and sub-threshold slope (SS). We observed that when the channel length is lesser, the junctionless transistor has better results for DIBL (34.61% improvement) and Ion/Ioff ratio (by 10^3). The results of SS for both the transistors were the same. These results varied when the channel lengths of the transistors were changed between 20 nm, 30 nm and 40 nm. All the device parameters are simulated in 2D environment using the device simulator Cogenda Visual TCAD. IndexTerms dual material dual gate (DMDG) Junctionless transistor, two-dimensional (2D) model, drain induced barrier lowering (DIBL), sub-threshold slope (SS), Cogenda Visual technology computer aided design (TCAD). ________________________________________________________________________________________________________
Ising Model on Locally Tree-Like Graphs: Uniqueness of Solutions to Cavity Equations

In the study of Ising models on large locally tree-like graphs, in both rigorous and non-rigorous methods one is often led to understanding the so-called belief propagation distributional recursions and its fixed points. We prove that there is at most one non-trivial fixed point for Ising models with zero or certain random external fields. Previously this was only known for sufficiently “low-temperature” models. Our main innovation is in applying information-theoretic ideas of channel comparison leading to a new metric (degradation index) between binary-input-symmetric (BMS) channels under which the Belief Propagation (BP) operator is a strict contraction (albeit non-multiplicative). A key ingredient of our proof is a strengthening of the classical stringy tree lemma of Evans-Kenyon-Peres-Schulman (2000). Our result simultaneously closes the following 6 conjectures in the literature: 1) independence of robust reconstruction accuracy to leaf noise in broadcasting on trees; 2) uselessness of global information for a labeled 2-community stochastic block model, or 2-SBM; 3) optimality of local algorithms for 2-SBM under noisy side information; 4) uniqueness of BP fixed point in broadcasting on trees in the Gaussian (large degree) limit; 5) boundary irrelevance in broadcasting on trees; 6) characterization of entropy (and mutual information) of community labels given the graph in 2-SBM.
Concept for Narrow-Band Filtering Rat-Race Coupler Using Dual-Mode Cross-Shaped Dielectric

A new concept for a narrow-band filtering rat-race coupler is reported, which is constructed by a dual-mode cross-shaped dielectric resonator (DR). By arranging the two modes of the dual-mode DR, the functions for a rat-race coupler are realised, and the bandpass filtering characteristic is integrated. Meanwhile, the single-cavity configuration results in a miniaturised size. An experiment on a fabricated prototype has validated this novel concept.
Region Proposal Network for Simple Objects in Grasping Experiments

Particularly in applied robotics, the grasping of objects is a major field which comes with various difficulties. Multiple objects with simple or complex shapes as well as different colors can be scattered on a surface in random positions and orientations. However, with the knowledge about the correct object positions, a robot has high chance of grasping them. Object detection systems can determine bounding boxes of objects in images, which can help to calculate the correct object positions. Current state-of-the-art object detection systems such as the popular Faster R-CNN and the Mask R-CNN, often use multi-stage architectures. Both models utilize a region proposal network to obtain regions which are likely to contain objects. This thesis introduces and evaluates multiple architecture variations of single-stage and two-stage models. These variations include a region proposal network, yet in the setting of grasping experiments. Usually, the training of these models is done in a supervised manner which requires lots of data with ground truth information. Generating this kind of data in a real world environment is expensive, yet it is cost-efficient to generate the same kind of data in a simulated environment. Therefore, this thesis introduces a framework to generate artificial data in a simulated grasping experiment environment. This framework implements several domain randomization techniques in order to randomize this simulation environment. The training data contains only artificial images with objects of simple geometry. The results have shown that models, which were trained only on these artificial images, can still generalize well to images of a real environment. Furthermore, the generalization to images which contain objects of complex geometry is equally possible. This thesis performs ablation studies on the employed domain randomization techniques which reveal both degradation and improvement of different techniques. Benchmarks on the model variations show that a significantly faster inference is possible compared to the originally Faster R-CNN and Mask R-CNN, while still achieving pleasant prediction results. This was made possible by using different configurations for the region proposal network, and by introducing faster feature extraction backbone architectures.
A Review on Intrusion Detection Systems to Secure IoT Networks

– The Internet of Things (IoT) and its rapid advancements will lead to everything being connected in the near future. The number of devices connected to the global network is increasing every day. IoT security challenges arise as a result of the large-scale incorporation of smart devices. Security issues on the Internet of Things have been the most focused area of research over the last decade. As IoT devices have less memory, processing capacity, and power consumption, the traditional security mechanisms are not suitable for IoT. A security mechanism called an Intrusion Detection System (IDS) has a crucial role in protecting the IoT nodes and networks. The lightweight nature of IoT nodes should be considered while designing IDS for the IoT. In this paper, the types of IDS, the major attacks on IoT, the recent research, and contributions to IDS in IoT networks are discussed, and an analytical survey is given based on the study. Though it is a promising area for research, IDS still needs further refinement to ensure high security for IoT networks and devices. Hence, further research, development, and lightweight mechanisms are required for IDS to provide a higher level of security to the resource-limited IoT network.
Hybrid Blockchain Database Systems: Design and Performance

With the emergence of hybrid blockchain database systems, we aim to provide an in-depth analysis of the performance and trade-offs among a few representative systems. To achieve this goal, we implement Veritas and BlockchainDB from scratch. For Veritas, we provide two flavors to target the crash fault-tolerant (CFT) and Byzantine fault-tolerant (BFT) application scenarios. Specifically, we implement Veritas with Apache Kafka to target CFT application scenarios, and Veritas with Tendermint to target BFT application scenarios. We compare these three systems with the existing open-source implementation of BigchainDB. BigchainDB uses Tender-mint for consensus and provides two flavors: a default implementation with blockchain pipelining and an optimized version that includes blockchain pipelining and parallel transaction validation. Our experimental analysis confirms that CFT designs, which are typically used by distributed databases, exhibit much higher performance than BFT designs, which are specific to blockchains. On the other hand, our extensive analysis highlights the variety of design choices faced by the developers and sheds some light on the trade-offs that need to be done when designing a hybrid blockchain database system.
Structure and Learning

This report documents the program and the outcomes of Dagstuhl Seminar 21362 “Structure and Learning”, held from September 5 to 10, 2021. Structure and learning are among the most prominent topics in Artificial Intelligence (AI) today. Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting “A 20 Year Roadmap for AI” at AAAI 2019. In this Dagstuhl seminar, we discussed related problems from an interdiscplinary perspective, in particular, Cognitive Science, Cognitive Psychology, Physics, Computational Humor, Linguistic, Machine Learning, and AI. This report overviews presentations and working groups during the seminar
Nutritional Status and its Concomitants of Two Contrasting Occupational Groups: A Study on Santal Community of Birbhum District, West Bengal, India

Background: It is well documented that nutritional status is essential for better productivity and overall health and well-being of the population and the nation at large. However, a large section of workers particularly in developing countries were still lagging behind the satisfactory level in nutritional condition that needs to be addressed. Therefore, the aim of the study was to compare the nutritional status and its concomitants between two contrasting occupational groups. Methods: Cross-sectional data on selected anthropometric, socio-economic variables, energy intake and expenditure were collected from 204 adult Santal participants, who were engaged as stone mine work and agricultural work. Results: Most of the anthropometric traits were similar between two groups, irrespective of sex except MUAC, WC and HC were greater among stone mine workers compared to the agricultural workers. Around 27% of males and 60% females of stone mine workers and around 40% of males and 34% females of agricultural workers suffer from chronic energy deficiency. Nutritional status of stone mine workers was influenced by education and calorie expenditure. Again, nutritional status of agricultural workers was influenced by economic condition and calorie expenditure. Conclusion: The study indicated that a considerable number of participants irrespective of occupation suffer from chronic energy deficiency. Education, economic condition and calorie expenditure of the participants are likely to influence nutritional status of both the labourer population.
Impact of Ag on the Limit of Detection towards NH3-Sensing in Spray-Coated WO3 Thin-Films

Ag-doped WO3 (Ag–WO3) films were deposited on a soda-lime glass substrate via a facile spray pyrolysis technique. The surface roughness of the films varied between 0.6 nm and 4.3 nm, as verified by the Atomic Force Microscopy (AFM) studies. Ammonia (NH3)-sensing measurements of the films were performed for various concentrations at an optimum sensor working temperature of 200 °C. Enrichment of oxygen vacancies confirmed by X-ray Photoelectron Spectroscopy (XPS) in 1% Ag–WO3 enhanced the sensor response from 1.06 to 3.29, approximately 3 times higher than that of undoped WO3. Limit of detection (LOD) up to 500 ppb is achieved for 1% Ag–WO3, substantiating the role of Ag in improving sensor performance.
Prevalence and antimicrobial sensitivity of Escherichia coli and Salmonella species in field cases of rabbit intestinal coccidiosis treated with prebiotic

Coccidian infection may promote the proliferation of gut bacteria of the family Enterobacteriaceae. Bacterial infections by members of this family in rabbits can induce a negative impact on their health and lead to high mortality, especially in young individuals. This study aimed to investigate the effect of prebiotic treatment on Escherichia coli and Salmonella species during natural intestinal coccidiosis in rabbits. Forty-five rabbits were selected from a rabbit farm in Beni-Suef, Egypt. Out of the 45 rabbits, 15 were coccidia-free and used as the negative control group (NC group) and 30 were naturally infected with coccidia. The infected rabbits were randomly divided into two equal groups, the positive control group (PC) and the prebiotic treated group (PT) which was orally treated with prebiotic for 8 successive days. Faecal oocyst count was assessed daily during the course of treatment. At 8 days post-treatment, 5 rabbits from each group were euthanised for the microbiological examination of the intestinal tract. On day 8 post-treatment, the PT group showed a significant ( P ≤0.05) reduction in the oocyst count (5.33 ×10 3 ± 0.89) with a significant ( P ≤0.05) decline in the prevalence of E. coli and Salmonella (66.7 % and 26.4%, respectively). The PC group showed highly a significant oocyst count (21.67×10 3 ± 0.82 OPG), with a significant increase in the prevalence of E. coli and Salmonella (86.7 % and 46.7 %, respectively). The NC group remained coccidian free and exhibited only E. coli with no Salmonella infection. The in vitro susceptibility test showed that E. coli isolates were highly resistant to most of the tested antimicrobials while Salmonella isolates showed variable resistance profiles. In conclusion, the prebiotic treatment significantly reduced the prevalence of E. coli and Salmonella infections coexisted with intestinal coccidiosis naturally infected rabbits.
A Label-Free Colorimetric Assay Based on Gold Nanoparticles for the Detection of H2O2 and Glucose

The significance of sensing hydrogen peroxide (H2O2) is due to its ubiquity, being a potential biomarker as well as an end-product of several oxidation reactions. Herein, based on gold nanoparticles (AuNPs) and coupled with single-stranded DNA (ssDNA) and ceria nanoparticles (CeO2), we developed a novel colorimetric method to detect H2O2 and glucose in NaCl solutions. In the presence of H2O2, ssDNA adsorbed on the surface of CeO2 could be released and subsequently decorated AuNPs, resulting in a distinct color change of the aqueous solution from purple to red, which could be observed by the naked eye. Since H2O2 can be produced in the process of glucose oxidation by glucose oxidase (GOx), this approach can also be employed to detect glucose. By employing this sensing system, the detection limits for H2O2 and glucose are about 0.21 μM and 3.01 µM, respectively. Additionally, monitoring the content of glucose in blood serum samples was successfully achieved by the proposed strategy. This work opens a potential avenue for the quantitative detection of H2O2 and glucose in clinical diagnostics.
Vitreous Humor Proteome: Targeting Oxidative Stress, Inflammation, and Neurodegeneration in Vitreoretinal Diseases

Oxidative stress is defined as an unbalance between pro-oxidants and antioxidants, as evidenced by an increase in reactive oxygen and reactive nitrogen species production over time. It is important in the pathophysiology of retinal disorders such as diabetic retinopathy, age-related macular degeneration, retinal detachment, and proliferative vitreoretinopathy, which are the focus of this article. Although the human organism’s defense mechanisms correct autoxidation caused by endogenous or exogenous factors, this may be insufficient, causing an imbalance in favor of excessive ROS production or a weakening of the endogenous antioxidant system, resulting in molecular and cellular damage. Furthermore, modern lifestyles and environmental factors contribute to increased chemical exposure and stress induction, resulting in oxidative stress. In this review, we discuss the current information about oxidative stress and the vitreous proteome with a special focus on vitreoretinal diseases. Additionally, we explore therapies using antioxidants in an attempt to rescue the body from oxidation, restore balance, and maximize healthy body function, as well as new investigational therapies that have shown significant therapeutic potential in preclinical studies and clinical trial outcomes, along with their goals and strategic approaches to combat oxidative stress.