Miracle-dz/UniFAR-FTUs huggingface dataset:
Physicochemical properties of methacrylate resin, calcium hydroxide, calcium silicate, and silicon-based root canal sealers

Introduction: The use of root canal sealer, together with core filing material, is essential to provide hermetic seal in root canal system. However, different types of root canal sealer materials exhibit different properties, which may affect the quality of root canal treatment. Objectives: The aim of the study was to compare pH, solubility, dimensional change, flow, working time, and film thickness of methacrylate resin (EndoRez), calcium hydroxide (Sealapex), calcium silicate (BioRoot RCS), and silicone-based sealer (GuttaFlow Bioseal). Material and methods: Solubility, dimensional change, flow, working time, and film thickness of root canal sealer materials were assessed based on ISO standard 6876/2012 recommendations. pH of sealer materials was measured using a pH meter. pH, solubility and dimensional change of sealers were evaluated on day 1, 7 and 14. Results: pH values of Sealapex, BioRoot RCS, and Guttaflow Bioseal ranged between 8.91 and 12.01, whereas EndoRez showed an average pH value of 6. pH of EndoRez increased significantly over time ( p < 0.05), while pH of BioRoot RCS decreased significantly ( p < 0.05). However, no significant pH change was observed in Sealapex and GuttaFlow Bioseal. BioRoot RCS showed the highest solubility ( p < 0.05), and solubility of all sealer materials increased from day 1 to 14, but no significant change ( p > 0.05) was noted in BioRoot RCS and GuttaFlow Bioseal, respectively. Dimensional change of BioRoot RCS was significantly higher ( p < 0.05), with no significant difference ( p > 0.05) between EndoRez and Sealapex at 1 and 14. GuttaFlow Bioseal exhibited the highest flow value ( p < 0.05). BioRoot RCS demonstrated the longest working time ( p < 0.05), while EndoRez showed the lowest film thickness, which was comparable to Sealapex ( p > 0.05). Conclusions: EndoRez is slightly acidic, while the remaining sealer materials are alkalines. All sealers con-firmed to ISO standard, except for solubility and film thickness of BioRoot RCS.
Evaluation of Change Detection Systems Based on Boltzmann Machine

Detection of the state changes in the system, which may result in the failure of the system, would be very important in various ﬁelds of real-world engineering. We propose a novel unsupervised method for detection of changes in the framework of Energy Based Model (EBM). The proposed method incorporates the Restricted Boltzmann Machine (RBM) and Conditional Restricted Boltz-mann Machine (CRBM) as EBM. The state change can be evaluated by observing the free energy in the RBM/CRBM. The proposed method is evaluated by using two sets of time series; one is a sin wave with Gaussian noises and another is the state in a CartPole system. Experimental results show that the free energy is an eﬀective indicator for detecting the state change and that CRBM have better ability for detecting the state change than RBM.
Cultural Barriers and Gender Differences: A Qualitative Study of Issues Associated with Online Education in Pakistan

COVID-19 has emerged as a disaster for people around the globe. Consequently, it has changed the way we live and do things in our everyday lives. This study analyzes the experiences of online education of university students during COVID-19 pandemic. It further explores the ways in which gender effects the online education of the students. The study was conducted in four conveniently selected universities of Lahore, Punjab. In-depths interviews were conducted with 16 research participants and data were analyzed by employing thematic analysis approach. The study found that students, particularly females, were facing loads of hurdles in attending online classes, including non-availability or frequent disconnection of internet, electricity load shading, lack of knowledge about using technology for online education, and time management. This study not only presents lived experiences of students but also suggests some constructive measures to address and curtail the challenges that are being faced by students during online education.
REverSe TRanscrIptase chain termination (RESTRICT) for selective measurement of nucleotide analogs used in HIV care and prevention

Abstract Sufficient drug concentrations are required for efficacy of antiretroviral drugs used in HIV care and prevention. Measurement of nucleotide analogs, included in most HIV medication regimens, enables monitoring of short‐ and long‐term adherence and the risk of treatment failure. The REverSe TRanscrIptase Chain Termination (RESTRICT) assay rapidly infers the concentration of intracellular nucleotide analogs based on the inhibition of DNA synthesis by HIV reverse transcriptase enzyme. Here, we introduce a probabilistic model for RESTRICT and demonstrate selective measurement of multiple nucleotide analogs using DNA templates designed according to the chemical structure of each drug. We measure clinically relevant concentrations of tenofovir diphosphate, emtricitabine triphosphate, lamivudine triphosphate, and azidothymidine triphosphate with agreement between experiment and theory. RESTRICT represents a new class of activity‐based assays for therapeutic drug monitoring in HIV care and could be extended to other diseases treated with nucleotide analogs.
Learning to Construct Nested Polar Codes: An Attention-Based Set-to-Element Model

As capacity-achieving codes under successive cancellation (SC) decoding, nested polar codes have been adopted in 5G enhanced mobile broadband. To optimize the performance of the code construction under practical decoding, e.g. SC list (SCL) decoding, artificial intelligence based methods have been explored in the literature. However, the structure of nested polar codes has not been fully exploited for code construction. To address this issue, this letter transforms the original combinatorial optimization problem for the construction of nested polar codes into a policy optimization problem for sequential decision, and proposes an attention-based set-to-element model, which incorporates the nested structure into the policy design. Based on the proposed architecture for the policy, a gradient based algorithm for code construction and a divide-and-conquer strategy for parallel implementation are further developed. Simulation results demonstrate that the proposed construction outperforms the state-of-the-art nested polar codes for SCL decoding.
Assessing Ecological Indicators for Remnant Vegetation Strips as Functional Biological Corridors in Chilean Vineyards

Mediterranean central Chile is globally recognized as a hotspot for terrestrial biodiversity due to its high endemism and massive habitat loss. However, within the rural landscape of central Chile, significant extents of natural areas remain, especially on less productive, steep slopes, and vegetation strips extending from the surrounding hills to agricultural areas. Accordingly, vegetation strips or corridors, within lowland farms, constitute key elements to support the conservation of biodiversity in rural landscapes. To assess the ecological performance of corridors in 22 commercials vineyards in central Chile, we characterized them in terms of width-, length-, area-, and perimeter-to-area ratios, as well as the number of connections with natural areas. Based on a set of previously defined ecological indicators (species, functional groups, and structural components), we compared their occurrence in corridors within vineyards and in the surrounding natural areas. We evaluated the effects of corridor attributes on the occurrence of the selected ecological indicators, using a generalized linear mixed model with each vineyard as a random factor. The area, width, and length of vegetation corridors varied widely (1.2–86.3 ha, 10.5–95 m, and 380–5000 m, respectively). We found significant differences in the occurrence of indicators between corridors and natural areas. All sampled ecological indicators in corridors showed a negative relationship with the distance to the nearest natural area. Vegetation strips within vineyards represent important opportunities for biodiversity conservation that significantly enhance habitat quality in the agricultural landscape for biodiversity and habitat connectivity.
Accurate Modeling by Convolutional Neural-Network Regression of Resonant Frequencies of Dual-Band Pixelated Microstrip Antenna

The modeling of the performance characteristics of pixelated microstrip antennas poses special difficulties as their geometries cannot be readily parametrized compared with the geometries of antennas with standard shapes. A methodology is presented for accurately modeling the two resonant frequencies of a dual-band pixelated microstrip antenna based on convolutional-neural-network (CNN) regression that takes a representation of the entire pixelated surface of the antenna as input. The predictive performance of the CNN and shallow and deep conventional feedforward neural-net architectures were compared; architectural and learning algorithm hyperparameters were determined by means of Bayesian optimization. The CNN achieved the best predictive results of all the networks with mean relative errors on test predictions of 0.13% and 0.22% for the two resonant frequencies, respectively.
Can Global, Extended, and Repeated Ransomware Attacks Overcome the User's Status Quo Bias and Cause a Switch of System?

Ransomware (RW) attacks’ effectiveness has increased causing far reaching consequences that are not fully understood. The ability to disrupt core services, the global reach, extended duration and the repetition has increased their ability to harm an organization. One aspect that needs to be understood better is the effect on the user. The user in the current environment is exposed to new technologies that might be adopted but there are also habits of using existing systems. The habits have developed over time with trust increasing in the organization in contact directly and the institutions supporting it. This research explores whether the global, extended and repeated RW attacks reduce the trust and inertia sufficiently to change long held habits in using information systems. The model tested measures the effect of the RW attack on the e-commerce status quo to evaluate if it is significant enough to overcome the user’s resistance to change.
Arti(cid:976)icial Intelligence in the Pharmacy Profession

The scientist named ‘John McCarthy’ used the term ”Arti(cid:976)icial Intelligence” for the very (cid:976)irst time in 1956. Previously, it is only limited to engineering (cid:976)ield, but in the recent years, it is brie(cid:976)ly introduced into the other (cid:976)ields like pharma, healthcare, business, public sector etc. This review article presented to help as a short presentation of AI for the doctors and pharmacists. Here we describe the AI in various (cid:976)ield of medical care, its advantage as well as disadvantages in pharmacy, and its tools. It is greatly advanced into the decision-making, problem solving and critical thinking and having applications in various (cid:976)ields like business, pharmacy, health care, and engineering as well. Now the robots are using in the various medical procedures as they are more trustworthy for doctors, as they are more advanced in their work, as they can do any task within the short time period and effectively than humans. This is concluded that AI is the new evolving (cid:976)ield in every sector, even in pharmacy, and it need more development for updating the current scenario as well as for new researches.
Influence of Residual Elements Contained in Steel Scrap for the Production of Nodular Cast Iron

Abstract Nodular cast iron is a fundamental material used in engineering. It has unique properties and is one of the most produced materials in the world nowadays. The production of nodular cast iron involves melting of raw materials such as steel scrap, pig iron, machining returns and alloy irons. With the development of increasingly technological steels through the addition of chemical elements to meet a specific application, there is an increasing difficulty in acquiring steel scrap content low alloy for the production of nodular cast iron. The chemical elements present in the steel scrap favor the appearance of unwanted phases and particles. The present study evaluated the effect of the addition of the elements copper, chromium, molybdenum and nickel in levels between 0.50% w and 1.0% w in the formation of nodular cast iron microstructure. While nickel and copper were evenly distributed in the matrix, chromium and molybdenum formed carbides. In addition, chromium strongly favored the formation of perlite in nodular cast iron and molybdenum, the martensite.
Running-in Analysis of Transmission Gear

The automotive industry's impact on development and improvement, reliability and lifetime, of components is significant. Automotive market encourages small and medium enterprises (SMEs) to enter this market, which is for SMEs a challenge to meet specified product quality. In this study, running-in wear analysis of transmission gears is conducted, which is rarely discussed in literature. Based on this study, one can make recommendations to SMEs to improve their product. Examination tests between an original equipment manufacturer (OEM) gear and SMEs gear product have been made. Results showed that mass and chemical composition are same. A clear difference between SMEs and OEM gear was seen from surface roughness and material hardness tests. SMEs gear exhibits a failure mechanism, and OEM gear does not indicate any failure indication during running-in period. © 2021 Published by Faculty of Engineering * Corresponding author: J. Jamari E-mail: j.jamari@gmail.com Received: 14 April 2021 Revised: 2 June 2021 Accepted: 1 August 2021
A circularity-based quality assessment tool to classify the core for recovery businesses

To realise circularity in the management of used materials and associated recovery practices for value creation, a circularity-based quality assessment tool (CQAT) is proposed in this study. The issue is addressed with core classification based on quality factors assessing circularity at the product level. A circularity-based quality indicator (QI) is developed that classifies the core into reusable, remanufacturable, and recyclable (3R) at the acquisition phases of the core retrieval system. For the validation of CQAT, four real cases of small-medium-sized dismantling firms dealing with engines, motor gearboxes, motor pumps, and refrigerators involved in remanufacturing activities are analysed. The study employs an integrative coefficient correlation maximisation approach (CCMA) and whitenization weight functions (WWF) clustering model that estimates the priorities of regenerative and restorative actions. The result reveals that the severe damage with high disassembly processing time due to destructive disassembly in the post-acquisition phase is highly critical to the circularity of the retrieval system. It exhibits that damage, processing time, value retention classify core into lowest grade at acquisition phases. Since possibilities of value recovery are lost, firms are least concerned with social and environmental considerations. Additionally, the design and functionality dimensions in the inspection majorly classify cores into remanufacturable.
Master-Slave Synchronization for Trajectory Tracking Error Using Fractional Order Time-Delay Recurrent Neural Networks via Krasovskii-Lur'e Functional for Chua's Circuit

This paper presents an application of a Fractional Order Time Delay Neural Networks to chaos synchronization. The two main methodologies, on which the approach is based, are fractional order time-delay recurrent neural networks and the fractional order inverse optimal control for nonlinear systems. The problem of trajectory tracking is studied, based on the fractional order Lyapunov-Krasovskii and Lur’e theory, that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a reference function is obtained. The method is illustrated for the synchronization, the analytic results we present a trajectory tracking simulation of a fractional order time-delay dynamical network and the Fractional Order Chua’s circuits.
A REVIEW OF FEATURE EXTRACTION METHODS ON MACHINE LEARNING

Extracting features from input data is vital for successful classification and machine learning tasks. Classification is the process of declaring an object into one of the predefined categories. Many different feature selection and feature extraction methods exist, and they are being widely used. Feature extraction, obviously, is a transformation of large input data into a low dimensional feature vector, which is an input to classification or a machine learning algorithm. The task of feature extraction has major challenges, which will be discussed in this paper. The challenge is to learn and extract knowledge from text datasets to make correct decisions. The objective of this paper is to give an overview of methods used in feature extraction for various applications, with a dataset containing a collection of texts taken from social media.
Interaction between dietary and habitat niche breadth influences cetacean vulnerability to environmental disturbance

Ecosystems are experiencing elevated levels of disturbance, and species with narrower niches are often more vulnerable to disturbances. Niche breadth is often measured in terms of either diet or habitat use but diet and spatial use are infrequently considered in tandem. These different aspects of niche breadth potentially expose species to different types of disturbances; species with narrow dietary niches may be more affected by disturbances that alter trophic relationships, while species with narrow habitat niches may be more vulnerable to habitat loss and point-source pollutants. We examined dietary and habitat niche breadth of common bottlenose dolphins, Tursiops truncatus truncatus, from three different nearshore sites in the Gulf of Mexico (GoM). Using stable isotopes, we determined proportional contributions of different prey groups to dolphin diets at each site and through time at one site. We used satellite-linked telemetry at two sites to determine habitat use and site fidelity. Additionally, we examined the literature on cetacean diet, habitat use, movement, and IUCN status to determine relationships between niche breadth and population status for different species. Dolphin diets varied among sites as available prey varied, but Perciformes fish were the most frequently consumed prey. At the site for which we had temporal data, dolphins consumed more cephalopods in 2015 and 2018 but otherwise consumed primarily Perciformes fish. Dolphins had small utilization distributions and exhibited high site fidelity. Data from 31 cetacean species revealed that most species with vulnerable, threatened, or endangered IUCN statuses not only have specialized diets but also exhibit high site fidelity. Dolphins had diet characteristic of flexible generalists but were habitat specialists with high site fidelity. Dolphin populations in the GoM may have altered their diets in response to environmental changes that have altered community composition and trophic dynamics. On the other hand, their high site fidelity has exposed them to point-source pollutants, such as oil spills, persistent organic pollutants, and freshwater. Our broader analysis of cetaceans confirmed that species with specialized diets and high site fidelity were the most vulnerable to disturbances, providing a framework to predict which nearshore dolphin populations, and cetaceans in general, are most vulnerable to environmental changes. v www.esajournals. org 1 September 2021 v Volume 12(9) v Article e03759
Proposal to determine the carbon and ecological footprint of seawater reverse osmosis desalination in the Canary Islands plants considering the energy mix

This study focuses on seawater reverse osmosis (SWRO) desalination plants in the Canary Islands (Spain) where there are more than 320 private and public units of varying size. The objective is to provide proposals to optimize the operation of these plants, improving energy consumption, water quality, costs and emissions, and making the desalination process more efficient and sustainable. An analysis is undertaken in this study of the carbon footprint ratios (per m3 and type of inhabitant or per m3 and type of productive activity) for each of the islands as a contribution to the decisionmaking process on the inclusion of renewable energy in the energy mix. The conditions for the production of freshwater in each of the islands vary due to differences in the available power technologies and the energy costs. The ecological footprint is also studied for each island. This work shows the results of an analysis of energy efficiency and environmental footprints. The conclusions of the study can serve as a tool to improve energy efficiency in SWRO plants. For an annual desalinated water production in the Canary Islands of 660,000 m3/d, and considering an energy consumption of about 3.04 kWh/m3, the calculated carbon footprint per year is 439,402 t CO2 and the ecological footprint 219,701 ha/y. Given a total population in the islands of 2,207,225 inhabitants, the ecological footprint is 0.1 ha/person/y, and the carbon footprint 0.2 t CO2/person/y.
In search of dense subgraphs: How good is greedy peeling?

The problem of finding the densest subgraph in a given graph has several real‐world applications, particularly in areas like social network analysis, protein, and gene networks. Depending on the application, finding dense subgraphs can be used to determine regions of high importance, similar characteristics, or enhanced interaction. The densest subgraph extraction problem is fundamentally a non‐linear optimization problem. Nevertheless, it can be solved in polynomial time by an exact algorithm based on iteratively solving a series of max‐flow subproblems. Despite its polynomial‐time complexity, the computing time required by exact algorithms on very large graphs could be prohibitive. Thus, to approach graphs with millions of vertices and edges, one has to resort to heuristic algorithms. We provide an efficient implementation of a greedy heuristic from the literature that is extremely fast and has some nice theoretical properties. We also introduce a new heuristic algorithm that is built on top of the greedy and the exact methods. An extensive computational study is presented to evaluate the performance of various algorithms on a benchmark composed of 86 instances taken from the literature and real world. This analysis shows that the proposed heuristic algorithm is very effective on a large number of test instances, often providing either the optimal solution or a near‐optimal solution within short computing times.