Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Neural Network Prediction of Mechanical Drilling Speed

Rate of penetration (ROP) prediction is critical for the optimization of drilling parameters and ROP improvement during drilling. However, it is still challenging to accurately predict ROP based on traditional empirical formula methods. This is usually the case for the development of the Wushi 17-2 oilfield block in the South China Sea. The Liushagang Formation is complex and the ROP is relatively low and difficult to increase. Ordinary data-driven ROP prediction models are not applicable because they do not take into account the complexity of formation conditions. In this work, we characterize the formation with acoustic transit time and build a data-driven ROP prediction model based on a deep neural network approach. By using the exploratory well data of the Wushi 17-2 oilfield for training and testing, the matching degree of the established model with the real data can reach 82%. In addition, we have developed a drilling parameter optimization process based on the ROP prediction model to improve ROP. Through on-site simulation, we found that the process can well meet the construction requirements. The established models and process flow are also applicable to the development of other formations and fields.
Ultra-compact micro-optical elements for generating high-order Bessel beams

Compared with traditional vortex beams, high-order Bessel beams (HoBBs) have the unique characteristics of non-diffraction and self-healing, which makes them more competitive in fields of complex-environment optical manipulation and long-distance optical communication. With the progress of machining technology, the realization of micro-nano-sized optical components becomes possible, and integrated optics becomes the trend of future development. However, up to now, the traditional devices for generating HoBBs have a large volume and high weight, which makes it difficult to achieve optical integration. Here, we have designed an ultra-compact micro-optical element to generate high-quality micro-sized HoBBs. Good morphology of the element prepared via laser direct writing (LDW), and beam characteristics of non-diffraction and self-healing were demonstrated experimentally.
Amazon Web Services in Action

Distributed systems are unpredictable, and it can be an enormous challenge to manage around potentially-crippling obstacles like hardware failures, unanticipated changes in load, and network issues. Amazon Web Services (AWS) is a platform for hosting distributed applications in a secure, flexible cloud environment. AWS provides a suite of services designed to keep the focus on what an application does instead of the infrastructure required to run it. Whether serving up blog pages, analyzing fast data in real-time, building software as a service, or implementing a massive e-commerce site, AWS provides both a stable platform and services that will scale with every application. Amazon Web Services in Action introduces readers to computing, storing, and networking in the AWS cloud. It starts with a broad overview of AWS, and shows how to spin up servers manually and from the command line. Then, it explores infrastructure automation with the AWS Cloud Formation service, where readers can describe a blueprint of their infrastructure as code. Readers will learn how to isolate systems using private networks to increase security, how to use the most valuable AWS managed services available on AWS, and about the benefits of stateless servers. In the end, they'll look to the AWS model for high availability, scaling, decoupling with queues and load balancers, and fault tolerance.
Explore the relationship between fractal dimensions of diffusion-limited aggregations and probabilities of traps on sites

The paper mainly focuses on exploring the relationship between fractal dimensions of diffusion-limited aggregation (DLA) models and traps on the field, that may consume particles. Diffusion-limited aggregation has been applied widely in various fields such as biology, physics, mathematics and engineering. Nevertheless, it has been noticed that one widespread problem exists among research on diffusion-limited aggregations, which is the frequent ignorance of particles loss during Brownian motion. Seldom has it been investigated, that the changes of structures of DLA models are due to environmental variations. After simulations using Java, through observing generated path, data table and scatter plot of each DLA model, it has been concluded that fractal dimensions of DLA models and probabilities of traps on sites of the field satisfy the relationship of a negative correlation under a certain range of probabilities of traps; which means that the increasing probabilities of traps leads to the decreasing of fractal dimensions of DLA models. The research verifies the conjecture that a number of researches over diffusion-limited aggregations may have their results influenced because of ignoring the particles loss problem.
Fraship: A Framework to Support End-User Personalization of Smart Home Services with Runtime Knowledge Graph

With the continuous popularization of smart home devices, people often anticipate using different smart devices through natural language instructions and require personalized smart home services. However, existing challenges include the interoperability of smart devices and a comprehensive understanding of the user environment. This study proposes Fraship, a framework supporting smart home service personalization for end-users. It incorporates a runtime knowledge graph acting as a bridge between users’ language instructions and the corresponding operations of smart devices. The runtime knowledge graph is used to reflect contextual information in a specific smart home, based on which a language-instruction parser is proposed to allow users to manage smart home devices and services in natural language. We evaluated Fraship on a real-world smart home. Our results show that Fraship can effectively manage smart home devices and services based on the runtime knowledge graph, and it recognizes instructions more accurately than other approaches.
Quantifying Resilience and Uncertainty in Coastal Flooding Events: Framework for Assessing Urban Vulnerability

AbstractFlood resilience is a desirable state of natural and built environments in dealing with hazards such as extreme rainfalls, storm surges, and hurricanes. Here, resilience means the capacity to reach an acceptable level of functionality during and after a flood. Observations of the recent history of flood disasters in New Orleans and New York City (NYC) have suggested the necessity of quantifying resilience in these regions utilizing a floodplain’s vulnerability assessment platform. Once a meaningful and well-recognized method is developed, then resources can be allocated to increase resiliency. In this study, redundancy, resourcefulness, robustness, and rapidity metrics based on socioeconomic, manmade, and natural conditions are developed to quantify coastal resilience. Exposure and susceptibility are also defined and evaluated for quantifying vulnerability. The proposed evaluation method, which is based on the linear combination of the developed metrics, uses a multicriteria decision making techni.
Analytical Models for Motifs in Temporal Networks

Dynamic evolving networks capture temporal relations in domains such as social networks, communication networks, and financial transaction networks. In such networks, temporal motifs, which are repeated sequences of time-stamped edges/transactions, offer valuable information about the networks’ evolution and function. However, calculating temporal motif frequencies is computationally expensive as it requires: First, identifying all instances of the static motifs in the static graph induced by the temporal graph. And second, counting the number of subsequences of temporal edges that correspond to a temporal motif and occur within a time window. Since the number of temporal motifs changes over time, finding interesting temporal patterns involves iterative application of the above process over many consecutive time windows. This makes it impractical to scale to large real temporal networks. Here, we develop a fast and accurate model-based method for counting motifs in temporal networks. We first develop the Temporal Activity State Block Model (TASBM), to model temporal motifs in temporal graphs. Then we derive closed-form analytical expressions that allow us to quickly calculate expected motif frequencies and their variances in a given temporal network. Finally, we develop an efficient model fitting method, so that for a given network, we quickly fit the TASMB model and compute motif frequencies. We apply our approach to two real-world networks: a network of financial transactions and an email network. Experiments show that our TASMB framework (1) accurately counts temporal motifs in temporal networks; (2) easily scales to networks with tens of millions of edges/transactions; (3) is about 50x faster than explicit motif counting methods on networks of about 5 million temporal edges, a factor which increases with network size.
Implementation of Exome Sequencing in Prenatal Diagnosis and Impact on Genetic Counseling: The Polish Experience

Background: Despite advances in routine prenatal cytogenetic testing, most anomalous fetuses remain without a genetic diagnosis. Exome sequencing (ES) is a molecular technique that identifies sequence variants across protein-coding regions and is now increasingly used in clinical practice. Fetal phenotypes differ from postnatal and, therefore, prenatal ES interpretation requires a large amount of data deriving from prenatal testing. The aim of our study was to present initial results of the implementation of ES to prenatal diagnosis in Polish patients and to discuss its possible clinical impact on genetic counseling. Methods: In this study we performed a retrospective review of all fetal samples referred to our laboratory for ES from cooperating centers between January 2017 and June 2021. Results: During the study period 122 fetuses were subjected to ES at our institution. There were 52 abnormal ES results: 31 in the group of fetuses with a single organ system anomaly and 21 in the group of fetuses with multisystem anomalies. The difference between groups was not statistically significant. There were 57 different pathogenic or likely pathogenic variants reported in 33 different genes. The most common were missense variants. In 17 cases the molecular diagnosis had an actual clinical impact on subsequent pregnancies or other family members. Conclusions: Exome sequencing increases the detection rate in fetuses with structural anomalies and improves genetic counseling for both the affected couple and their relatives.
Mechanism of GAPDH Redox Signaling by H2O2 Activation of a Two−Cysteine Switch

Oxidation of glyceraldehyde−3−phosphate dehydrogenase (GAPDH) by reactive oxygen species such as H2O2 activate pleiotropic signaling pathways is associated with pathophysiological cell fate decisions. Oxidized GAPDH binds chaperone proteins with translocation of the complex to the nucleus and mitochondria initiating autophagy and cellular apoptosis. In this study, we establish the mechanism by which H2O2−oxidized GAPDH subunits undergo a subunit conformational rearrangement. H2O2 oxidizes both the catalytic cysteine and a vicinal cysteine (four residues downstream) to their respective sulfenic acids. A ‘two−cysteine switch’ is activated, whereby the sulfenic acids irreversibly condense to an intrachain thiosulfinic ester resulting in a major metastable subunit conformational rearrangement. All four subunits of the homotetramer are uniformly and independently oxidized by H2O2, and the oxidized homotetramer is stabilized at low temperatures. Over time, subunits unfold forming disulfide−linked aggregates with the catalytic cysteine oxidized to a sulfinic acid, resulting from thiosulfinic ester hydrolysis via the highly reactive thiosulfonic ester intermediate. Molecular Dynamic Simulations provide additional mechanistic insights linking GAPDH subunit oxidation with generating a putative signaling conformer. The low−temperature stability of the H2O2−oxidized subunit conformer provides an operable framework to study mechanisms associated with gain−of−function activities of oxidized GAPDH to identify novel targets for the treatment of neurodegenerative diseases.
Physiactisome: A New Nanovesicle Drug Containing Heat Shock Protein 60 for Treating Muscle Wasting and Cachexia

Currently, no commercially available drugs have the ability to reverse cachexia or counteract muscle wasting and the loss of lean mass. Here, we report the methodology used to develop Physiactisome—a conditioned medium released by heat shock protein 60 (Hsp60)—overexpressing C2C12 cell lines enriched with small and large extracellular vesicles. We also present evidence supporting its use in the treatment of cachexia. Briefly, we obtain a nanovesicle-based secretion by genetically modifying C2C12 cell lines with an Hsp60-overexpressing plasmid. The secretion is used to treat naïve C2C12 cell lines. Physiactisome activates the expression of PGC-1α isoform 1, which is directly involved in mitochondrial biogenesis and muscle atrophy suppression, in naïve C2C12 cell lines. Proteomic analyses show Hsp60 localisation inside isolated nanovesicles and the localisation of several apocrine and merocrine molecules, with potential benefits for severe forms of muscle atrophy. Considering that Physiactisome can be easily obtained following tissue biopsy and can be applied to autologous muscle stem cells, we propose a potential nanovesicle-based anti-cachexia drug that could mimic the beneficial effects of exercise. Thus, Physiactisome may improve patient survival and quality of life. Furthermore, the method used to add Hsp60 into nanovesicles can be used to deliver other drugs or active proteins to vesicles.
On the stability of acetate in subduction zone fluids

Recent theoretical studies have suggested that, under certain intermediate redox conditions, organic anions, in particular acetate, may be the dominant carbon species in deep subduction zone fluids. This could have major consequences for the properties of these fluids, for the formation of diamonds, and for the deep carbon cycle in gen-eral. We have tested these predictions by carrying out both ex situ piston cylinder experiments and in situ Raman spectroscopic experiments in the externally heated diamond cell, with a temperature up to 600 °C and pressure up to 5 GPa, the predicted stability field of acetate. We observed that upon heating and pressurisation to these conditions, an aqueous solution of sodium acetate undergoes several inter-esting reactions, including the formation of higher hydrocarbons. However, at 5 GPa and 600 °C, almost all organic species appear to have decomposed to a graphite-like material. Our experiments, therefore, do not support the stability of acetate and other organics as main carbon species in deep subduction fluids. However, the stability of minor concentrations of such species is still possible and requires further study. During deep subduction, most of the reduced carbon in sediments may be retained and recycled into the mantle to great depth.
COVID-19 Return to Sport: NFL Injury Prevalence Analysis

Background Sport injuries have been common among athletes across the globe for decades and have the potential to disrupt athletic careers, performance, and psyche. Many health professionals and organizations have undertaken injury mitigation strategies to prevent sport injuries through protective equipment, training protocols, and a host of other evidence-based practices. Many of these specialized training methods were disrupted due to protocols to mitigate the spread of COVID-19. This research examines the effects of the COVID-19 pandemic in relation to the prevalence of athletic injuries in the National Football League (NFL). Objective During the COVID-19 pandemic, NFL teams and athletes across all levels of sport were reported to have reduced training in preparation for their seasons due to protocols to mitigate the spread of COVID-19. This study compares the prevalence of injury during the 2018, 2019, and 2020 NFL seasons, with the aim to determine the potential causes of the differences in injury prevalence. Methods Official injury reports from each team were counted during the 17-week regular season of each year (2018, 2019, and 2020). The data were analyzed using an unpaired t test to compare the injury prevalence between each of the three seasons. Results The 2018 season produced a total of 1561 injuries and a mean of 48.8 injuries per team. The 2019 season produced a total of 1897 injuries and a mean of 59.3 injuries per team, while the 2020 season produced a total of 2484 injuries and a mean of 77.6 injuries per team. An unpaired t test was performed using the data to compare the mean number of injuries per team during each of the seasons. Comparison of the 2020 season against the 2019 season showed a statistically significant difference (P<.001); comparison of the 2020 season to the 2018 season found a statistically significant difference (P<.001); and comparison between the 2019 and the 2018 seasons found a statistically significant difference (P=.03). Conclusions Although the 2019 and 2018 seasons showed a statistically significant difference (P=.03), this difference is not as large when we compare the 2020 seasons versus the 2019 (P<.001) and 2018 (P<.001) seasons. The astronomical increase in injury prevalence during the 2020 season over the previous years raises the possibility that there was a reduced physiological adaptation to stress, due to the limited amount of training as a result of the closure of practice facilities in order to slow the spread of COVID-19.
Towards Automated Technologies in the Referencing Quality of Wikidata

Wikidata is a general-purpose knowledge graph with the content being crowd-sourced through an open wiki, along with bot accounts. The Wikidata data model enables assigning references to every single statement. Currently, there are more than 1 billion statements in Wikidata, of which about 70% have got references. Due to the rapid growth of Wikidata, the quality of Wikidata references is not well covered in the literature. To cover the gap, we suggest using automated tools to verify and improve the quality of Wikidata references. For verifying reference quality, we develop a comprehensive referencing assessment framework based on Data Quality dimensions and criteria. Then, we implement the framework as automated reusable scripts. To improve reference quality, we use Relation Extraction methods to establish a reference-suggesting framework for Wikidata. During the research, we managed to develop a subsetting approach to create a comparison platform and handle the big size of Wikidata. We also investigated reference statistics in 6 Wikidata topical subsets. The results of the latter investigation indicate the need for a wider assessment framework, which we aim to address in this dissertation.
Chapter VIII A Review of Methodologies for Analyzing Websites

This chapter is an overview of the process of Web analytics for Websites. It outlines how basic visitor information such as number of visitors and visit duration can be collected using log files and page tagging. This basic information is then combined to create meaningful key performance indicators that are tailored not only to the business goals of the company running the Website, but also to the goals and content of the Website. Finally, this chapter presents several analytic tools and explains how to choose the right tool for the needs of the Website. The ultimate goal of this chapter is to provide methods for increasing revenue and customer satisfaction through careful analysis of visitor interaction with a Website.
Technology Growth Ranking Using Temporal Graph Representation Learning

A key component of technology sector business strategy is understanding the mechanisms by which technologies are adopted and the rate of their growth over time. Furthermore, predicting how technologies grow in relation to each other informs business decision-making in terms of product definition, research and development, and marketing strategies. An important avenue for exploring technology trends is by looking at activity in the software community. Social networks for developers can provide useful technology trend insights and have an inherent temporal graph structure. We demonstrate an approach to technology growth ranking that adapts spatiotemporal graph neural networks to work with structured temporal relational graph data.
Tools for the Evaluation of Simulation and Testbed Scenarios

This document describes tools for the evaluation of simulation and testbed scenarios used in research on Internet congestion control mechanisms. We believe that research in congestion control mechanisms has been seriously hampered by the lack of good models underpinning analysis, simulation, and testbed experiments, and that tools for the evaluation of simulation and testbed scenarios can help in the construction of better scenarios, based on better underlying models. One use of the tools described in this document is in comparing key characteristics of test scenarios with known characteristics from the diverse and ever-changing real world. Tools characterizing the aggregate traffic on a link include the distribution of per-packet round-trip times, the distribution of connection sizes, and the like. Tools characterizing end-to-end paths include drop rates as a function of packet size and of burst size, the synchronization ratio between two end-to-end TCP flows, and the like. For each characteristic, we describe what aspects of the scenario determine this characteristic, how the characteristic can affect the results of simulations and experiments for the evaluation of congestion control mechanisms, and what is known about this characteristic in the real world. We also explain why the use of such tools can add considerable power to our understanding and evaluation of simulation and testbed scenarios.
Concept Annotation from Users Perspective: A New Challenge

Text data is highly unstructured and can often be viewed as a complex representation of different concepts, entities, events, sentiments etc. For a wide variety of computational tasks, it is thus very important to annotate text data with the associated concepts / entities, which can put some initial structure / index on raw text data. However, It is not feasible to manually annotate a large amount of text, raising the need for automatic text annotation. In this paper, we focus on concept annotation in text data from the perspective of real world users. Concept annotation is not a trivial task and its utility often highly relies on the preference of the user. Despite significant progress in natural language processing research, we still lack a general purpose concept annotation tool which can effectively serve users from a wide range of application domains. Thus, further investigation is needed from a user-centric point of view to design an automated concept annotation tool that will ensure maximum utility to its users. To achieve this goal, we created a benchmark corpus of two real world data-sets, i.e., “News Concept Data-set” and “Medical Concept Data-set”, to introduce the notion of user-oriented concept annotation and provide a way to evaluate this task. The term “user-centric” means that the desired concepts are defined as well as characterized by the users themselves. Throughout the paper, we describe the details about how we created the data-sets, what are the unique characteristics of each data-set, how these data-sets reflect real users perspective for the concept annotation task, and finally, how they can serve as a great resource for future research on user-centric concept annotation.