Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing the Conformational States of Thimet Oligopeptidase in Solution

Thimet oligopeptidase (TOP) is a metallopeptidase involved in the metabolism of oligopeptides inside and outside cells of various tissues. It has been proposed that substrate or inhibitor binding in the TOP active site induces a large hinge-bending movement leading to a closed structure, in which the bound ligand is enclosed. The main goal of the present work was to study this conformational change, and fluorescence techniques were used. Four active TOP mutants were created, each equipped with a single-Trp residue (fluorescence donor) and a p-nitro-phenylalanine (pNF) residue as fluorescence acceptor at opposite sides of the active site. pNF was biosynthetically incorporated with high efficiency using the amber codon suppression technology. Inhibitor binding induced shorter Donor-Acceptor (D-A) distances in all mutants, supporting the view that a hinge-like movement is operative in TOP. The activity of TOP is known to be dependent on the ionic strength of the assay buffer and D-A distances were measured at different ionic strengths. Interestingly, a correlation between the D-A distance and the catalytic activity of TOP was observed: the highest activities corresponded to the shortest D-A distances. In this study for the first time the hinge-bending motion of a metallopeptidase in solution could be studied, yielding insight about the position of the equilibrium between the open and closed conformation. This information will contribute to a more detailed understanding of the mode of action of these enzymes, including therapeutic targets like neurolysin and angiotensin-converting enzyme 2 (ACE2).
Design and Fabrication of a Wireless Strain Gage for Gas Turbine Engine Applications

This thesis describes the design and fabrication of a wireless strain gage (WSG) prototype that utilizes a radio-frequency (RF) transponder for the compressor section of a gas turbine engine. The passive transponder will be printed, welded or deposited directly onto the compressor blades, and thus several key issues have to be addressed in the design of the distributed-element microwave circuit. Some of these issues are the temperature inside the engine, which may vary from 300 °F to 1,600 °F; the large "g" forces experienced by the blades rotating at 12,000 RPM; the RF transponder thickness, which should be below that of the boundary layer thickness (800 microns) so the gas flow path through the engine is not affected. The footprint of the RF transponder circuit should not be larger than a few millimeters in any direction to accurately measure strain The proposed WSG concept employs a capacitive/inductive RF transponder design with a specific resonant frequency, which responds to a short band pulse of energy from a transceiver module, such that the return signal has been modulated by the strain transmitted by the component. The goal is to correlate the frequency shift of the modulated signal to the strain in the substrate. The specifics of the transceiver module are beyond the scope of this research. However, a literature review was conducted to determine some possible technologies and approaches to solve this problem. Specifically, this research explored four different approaches for the design and fabrication of RF transponders; including one based on thin film planar structures; one based on thick film technology; one based on a "free standing" structure with a buckled-beam capacitor and one with an antenna being the actual sensing element. Results from this investigation have shown that the "free standing" structures yielded the largest gage factor, i.e. 1000, compared to the thin-film and thick-film transponders which had gage factors between 11 and 14.
Design of a Novel Series of Hetero-Binuclear Superhalogen Anions MM′X4 − (M = Li, Na; M′ = Be, Mg, Ca; X = Cl, Br)

A series of hetero-binuclear superatom motifs involving chloride/bromide ligands, that is, MM′X4 − (M = Li, Na; M′ = Be, Mg, Ca; X = Cl, Br) anions, have been characterized by using many-body perturbation theory calculations. Large vertical electron detachment energies (VDEs, 5.470–6.799 eV) confirm the superhalogen identity of these anions. A larger VDE value can be obtained by introducing small M or large M′ central atoms and small halogen ligand atoms. Thus, one isomer of LiCaCl4 − possesses the largest VDE value. Besides, when the extra electron is shared by all ligand atoms or three bridging ligand atoms, the isomers have relatively larger VDE values.
The peculiarities of Adaptation of First-Year Students to the University during COVID-19 Pandemic in Russia

The speed and efficiency of student adaptation largely determines the success of the educational process. This is especially true for university freshmen, as the older adolescent organism, with its specific neurophysiological characteristics, is very sensitive to changes in the environment. The situation with SARS-CoV-2 virus (COVID-19) pandemic has added to the problem of adaptation. The present study aims to examine the influence of the COVID-19 pandemic on the first-year students’ adaptation to university studies in Russia. The study involves 692 freshmen with an average age of 18.7, majority of women (80.6%), from three Russian universities: Kazan National Research Technological University, Kazan Federal University and Kazan Aviation University. The results show that during the COVID-19 period statistically significant changes occurred in almost all the adaptation components: physiological adaptation (p < .0001), socio-psychological adaptation (p < .05), academic adaptation (p < .05). There was a decrease in the level of physiological adaptation, while socio-psychological adaptation and academic adaptation increased. Comparison by gender during the COVID-19 period shows differences between adaptation processes of men and women. Comparison of foreign students with Russian citizens during the COVID-19 period demonstrates that foreign students stand out in socio-cultural adaptation (4.94 - foreigners, 4.64 - citizens of Russia, p < .05), physiological adaptation (5.36 - foreigners, 4.36 - citizens of Russia, p < .00001) and academic adaptation (5.28 - foreigners, 4.99 - citizens of Russia, p < .05)
Factorial Analysis to Categories Spread and Effect of Ebola Virus from Various Countries

: The main objective of this research analyze and correlates the number of cases and death rates reported on the Ebola virus in many countries. Ebola virus is one of the most lethal diseases to infect humans. This approach Proceeding uses the Factorial analysis technique in the Ebola Virus dataset. This method takes the largest common variance from all criteria and combines it into a single score. The analysis is applied to reported and categorize the most effective way countries using the counts of cases and deaths. To this investigate get it, and communicate data in a way of graphic factual properties. The analytic results on the Ebola virus dataset are not accessible anywhere. The Analysis was done by R Studio.
Concept of ‘Sail by Wire’ controller for a ship’s propulsion system from an unmanned ship perspective

ABSTRACT The intention of this paper is to present the idea of total ship control called the Supervisory ‘Sail by Wire’ Control System. As a part of it, there is an optimal controller of the ship’s propulsion plant presented in this paper. The paper focuses on the integration of four control subsystems of ships: navigation, propulsion, auxiliary and deck machinery systems, hull structures and cargo handling equipment. Such a controller can be build also on the base of conventional, fuzzy, or neural principles. Critical situations are detected, warned in advance is given about them, and they are reported to the ship's operator. The analytical results and recommendations as well as real vessel conditions will be recorded and post-processed. The intelligent integration of acquired data from subsystems will ensure that the control commands and actions compromise the multi-effects of a vessel's dynamics and operation. While performing accurate regulation and governing functions, this control system should have the capability of identifying and compensating failures during the ship's operation. The ‘Sail by Wire’ Supervisory Controller delivers a comprehensive study and a new idea to common European strategies and decisions for tracking issues in shipping in critical situations.
Distributed Finite-Time Multiagent Control for DC Microgrids With Time Delays

This paper proposes a novel distributed multi-agent finite-time control strategy with time delays for the state of charge balancing and voltage restoration in a dc microgrid with distributed battery energy storage systems. The delays can be different and theoretically unbounded for each battery system. Feedback linearization method is applied to convert the state of charge balancing and voltage restoration problems to double-integrator and single-integrator systems with input time delays, respectively. Then, the Artstein transformation is applied to reduce the time delayed systems to delay-free systems. Based on the reduced models, the finite-time control is modified to achieve the state of charge balancing and voltage restoration. Only the state of charge and its derivative and voltage information are required to be transmitted over a sparse communication network to generate the control signals. The finite-time Lyapunov method ensures accurate convergence and finite-time stability. The proposed secondary control strategy can be integrated with conventional primary droop control. The proposed control strategy is resilient to communication link failures and features plug-and-play capability. The performance is verified with an RTDS Technologies real-time digital simulator, using switching converter models and nonlinear lead-acid battery models.
VisualGPT Supplementary

Image and Word Features. Following [1], we use a Faster R-CNN networks [10] with ResNet-101 [5] as a backbone to train on Visual Genome dataset [8], and we extract a 2048-dimensional feature vector for each object. We use the Byte Pair Encoding (BPE) [12], which effectively incorporate sub-word information and is beneficial for dealing with out-of-vocabulary words. We employ learnable positional encoding and initialize token embedding from pretrained weights of GPT-2. Architecture and Hyperparameters. We have 3 layers in the encoder and 12 layers in the decoder with 12 heads in each layer. The hidden size D in each layer is 768. We load the GPT-2 (small) pretrained weights, which has 117M parameters into the decoder. We use the learning rate of 1e−4 under XE loss and 1e−5 during the reinforcement learning. We train the models with the AdamW optimizer [9] and a batch size 25. The beam size is equal to 5. The threshold τ is tuned on the validation set for different training data. Training Details. We train all the models in two steps. We first train the models with cross-entropy (XE) loss and then finetune them using reinforcement learning. The crossentropy loss LXE is the traditional autoregressive classification loss
Extremophiles: Applications and Adaptive Strategies

: Limits of life on earth is still an expanding topic and extensive research is being carried out throughout the world. These limits are the nether and higher boundaries of various parameters like atmospheric, geochemical, temperature and water/nutrient availability. By the means of evolution many microorganisms have evolved and adapted to harsh extreme conditions whose study can give us insight into their defense mechanisms and adaptive strategies. The secondary metabolites as well as enzymes which are stable over a broad range of conditions can be extracted and put forward for the betterment of the environment and humankind. This review focuses on extremophiles living in the extreme environmental conditions of temperature, radiation, desiccation, salinity and pH.
Using Contacts During Robot Manipulation to Map and Reconstruct a Scene

—Robot grasping and manipulation in real world scenarios typically imply i) objects of unknown shape, ii) cluttered, unstructured settings with a high degree of uncertainty, and iii) contacts between the grasped object and obstacles in the environment. In this paper we present an approach to reconstruct the shape of a grasped object and map the environment by measuring the contact forces while a robot tries to retrieve an object from a scene. We use a probabilistic approach that computes the probability of occupancy of two 3 dimensional maps (one for the grasped object and one for the environment), where the update on each map informs the construction of the other. Our method relies only on the force and torque measurements from the robot end-effector to find the line of action of the interaction forces, compute the possible contact locations and populate the two occupancy maps. Here we present our method, that we named SMORE (Simultaneous Mapping and Object Reconstruction), and discuss some future developments of this approach.
Structure-borne sound sources in buildings – Estimating the uncertainty of source properties and installed power from interlaboratory test results

A low immission due to structure-borne sound sources is a major component of the acoustic quality inside buildings. After many years of research, methods have been standardised to characterise such sources (EN 15657) and to predict their impact in buildings (EN 12354-5). This contribution is dedicated to the question what the uncertainty of the source descriptors and the predicted installed sound power is. To answer this question, an interlaboratory test with an artificial source was performed. Altogether seven laboratories participated, and estimates for the uncertainties of the source quantities could be deduced from the measurement results. Additionally, measurements were performed with a standardised structure-borne sound source, the ISO tapping machine, by all participating laboratories. The measured source quantity for this source turned out to be in good agreement with the theoretically predicted values thereby validating this theoretical prediction.
Resisting the Colonial Narrative of the Legend of Paoay Lake: Recounting a Story of Biag nga Agnanayon/Life Eternal in an Ilokano Barrio in Paoay, Ilocos Norte

This study embarked on the question: why would I allow a story full of colonial elements to become my legend or the story of the space of my existence? Thus, in this study, the author attempted to recount alternative stories about Paoay Lake, specifically from the experiences of people living in the Barrio Suba, of the Municipality of Paoay, province of Ilocos Norte, Philippines. Analyzing from the lens of two emerging concepts in Ilokano and Amianan Studies, pakasaritaan and purok ti isip, as well as using Ilokano data collection methods, this study was able to recount two stories of rituals, the ritual of digos, (ritual of naming while bathing in the dakkel a danum or Paoay Lake), and the ritual of gulgol, or the cleansing ceremony. Towards the conclusion of this paper, the author will lay out the alternative lens of the narrative about Paoay, which in this paper he has tentatively viewed as a manifestation of the Ilokano belief of Biag nga Agnanayon or Life Eternal as both rituals require the continuous passing of knowledge, the keeping of records, and the telling of stories; and as stories carry memories of life, as long as this is passed, kept, and told, it can be said that life is sustained, or life eternal. This study therefore suggests a turnaround from the colonial narrative of the Legend of Paoay Lake but to focus the storytelling now on the experiences of the people and on the writing of stories that will allow them to connect, reflect, react, and own the stories.
WThreadAFL: Deterministic Greybox Fuzzing for Multi-thread Network Servers

Multi-thread network servers have non-deterministic behaviors during fuzzing process, which constrains the performance of the fuzzer. In this work, we present WThreadAFL, a new greybox fuzzer for fuzzing multi-thread network servers. WThreadAFL addresses the non-deterministic problem based on a lightweight thread identification approach. We distinguish between worker thread and background thread via thread-context instrumentation, and only update coverage feedback of the worker thread. The experimental results on four popular server benchmarks show that, WThreadAFL behaves more deterministic than network protocol greybox fuzzer AFLNET, which increases the stability metric by 1.9%-25.7% and cuts down coverage variable edges by 19.0%-39.8% within 24 hours.
Real Time Face Recognition System Using Convolutional Neural Network

: Nowadays, face recognition is a reliable and accurate access control technique which is used along with other biometric methods. Face recognition is a process of recognizing the face sample of particular person from the set of face samples stored in the system. It can be done by converting images into digital binary representation to extract features using various techniques. In this paper, we propose a real time face recognition system which implemented using VGG16 architecture of Convolutional Neural Networks (CNN) and Transfer Learning. Convolutional Neural Networks are special type of deep learning neural networks which mainly used for image classification. OpenCV open-source library is used for image pre-processing. The experimental results indicates that an average accuracy of 97-99% is achieved and hence this is one of the best methods for face recognition.
The challenges of digital marketing of food products towards the behavior of young consumers

This article provides up-to-date information on the marketing of foods and non-alcoholic beve- rages to children and the changes that have occurred, focusing in particular on the major shift to digital marketing. It examines trends in media use among children, marketing methods in the new digital media landscape and children’s engagement with such marketing. It also considers the impact on children and their ability to counter marketing as well as the implications for children’s digital privacy. The aim of the article is to summarize the evidence on children’s exposure to food marketing in digital media and the persuasive power of that exposure’ overview international and national literature as well as to answer on questions regarding to frequency of using social media by children, methods used to track online and offline, influence of marketing on children exposure, impact of food marketing in social media for children and counter it and policies which should be implemented to protect children by food marketing.
KubeKlone: A Digital Twin for Simulating Edge and Cloud Microservices

Microservices are terraforming the computing landscape with web-scale infrastructures (e.g., Facebook, Google, Amazon) and telecom infrastructures (e.g., ATT, Ericsson) adopting them. At it’s core, the microservices paradigm promotes a decoupling of applications into multiple services – a decoupling that promotes better scalability, fault-tolerance, and deployability. Unfortunately, this decoupling significantly increases the space of configuration options and performance problems, rendering traditional approaches to management ineffective. Recent efforts to address this problem embrace Artificial Intelligence for IT Operations (AIOps). However, training effective AI models requires significant amounts of data and, in some instances, a framework for quickly exploring or analyzing model performance. Digital twins, or simulators, have effectively enabled AI-based management frameworks within other domains (e.g., manufacturing, industrial and automotive). In this paper, we propose the design of KubeKlone, the first comprehensive and opensource digital twin for modeling cloud-native microservices applications. KubeKlone is motivated by our need for accurate, efficient, and general model training. KubeKlone satisfies these goals by decoupling the simulation of microservices from the training of machine learning (ML) models while simultaneously ensuring efficiency and simplifying model design. In particular, KubeKlone introduces a queue-based simulator that abstracts infrastructure details and focuses on modeling, with queues, resource contentions across host and network components. To simplify model design, KubeKlone provides interfaces that hide simulator details and provides wrappers around popular ML packages. To illustrate the strengths of KubeKlone, we validate it against a deployment on Google Cloud Engine and implement several AIOps resource management algorithms (including PARTIES).
Incremental Exception Resolution in Tuplex

As data scientists scale their workloads, the cost of development scales accordingly because the time it takes to execute a single query increases with the size of the input data. Due to dirty data and unexpected input values, data scientists must test and iterate upon their pipelines to make them resilient to failures. Each time they update their pipeline, data scientists must re-execute the entire workload even if the change only affects a small number of error-causing rows. Throughout this process, data scientists bear the costs of unnecessary computations that state-of-the-art systems, such as Apache Spark, repeatedly perform over the majority of rows that do not produce failures. In this thesis, we present an efficient workflow for incrementally resolving errors that occur due to malformed input or unexpected values. To do so, we build upon Tuplex, a data analytics framework for Python. By removing unnecessary computation steps, incremental exception resolution allows data scientists to resolve exceptions that occur during pipeline execution efficiently. Experimentally, our implementation demonstrated a 37.06% end-to-end runtime savings over the current version of Tuplex when compared using a real-world dataset and pipeline. The efficiency improvements of incremental exception resolution correspond to significant impacts on time and money savings as well as a reduction in the environmental impact of data science development.
Enabling Innovation in Spectrum Management

We argue that the capital expenditures made by an individual mobile network operator is extremely high and risky. Also, radio spectrum sharing still lacks intelligence in the current architecture of mobile networks and needs to be rethought. We propose that a goal for the future mobile network architecture that shall be able to free mobile network operators from having to hold spectrum licenses and natively enable intelligent radio spectrum sharing among multiple mobile network operators. On the basis of the design principles, the duty of a single mobile network operator is split into two roles, one focuses on infrastructure development, the other only contains authorizations on the radio spectrum usage. We introduce a new role to the mobile network architecture, named Spectrum Trader, is a primary broker for spectrum trading, and it is used to coordinate with the demand-side requests and the supply-side resources to drive demand in a real-time bidding manner. We also introduce a concept of spectrum embedding that shall enable efficient and intelligent spectrum allocation by recommending the right spectrum bands based on user scenario. Finally, several challenges that need to be addressed in practical deployment are investigated.
Simulation Analysis of the FIN Height Influence on the Electrical Parameters of Junctionless Nanowire Transistors

This work analyzes the effects of the fin height on the electrical parameters of junctionless transistors through experimentally calibrated 3-D simulations. Results show that for long channel devices the better compromise is obtained with higher fin height, with higher ION/IOFF and smaller values of SS and DIBL, whereas for short channel ones the better compromise is found with smaller fin height, due to the reduced SS and DIBL and increased ION/IOFF ratio.