Miracle-dz/UniFAR-FTUs huggingface dataset:
DENTAL DISEASES DETECTION SYSTEM USING pH SALIVA-A REVIEW

Saliva, science has revealed, is a great deal greater than water. A Saliva is the complicated mixture of fluids that surrounds the oral tissues, and it originates from essential and minor salivary glands and non glandular sources such as crevicular fluids, oral microorganisms and host cells. The consistency of saliva can be watery, thick, sticky or frothy depending on its composition. the amount of proteins in saliva essentially will determine its thickness or frothiness. A basal unstimulated secretion is produced constantly to moisturize and lubricate the oral tissues for greater than 90 percentage of the day. gustatory, olfactory or pharmacological stimuli enlarge the production and secretion of saliva. Saliva is an increasing number of extra known as an captivating diagnostic fluid. The presence of a wide variety of sickness signaling salivary biomarkers that precisely replicate everyday and ailment states in people and the sampling advantages in contrast to blood sampling are some of the motives for this recognition. This explains the burgeoning research difficulty in assay features and technological developments for the detection of quite a variety salivary biomarkers to enhance scientific diagnosis, management, and treatment. Saliva is an amorphous fluid and, we comprehend two about its plenty of properties, rather little is known about the element of its molecular content. This complete know-how made it tough or even not possible to behavior scientific discovery research to stumble on salivary biomarkers that need to be used for stickiness detection With funding assist from NIDCR, numerous research centers in the United States tackled this assignment by way of first defining and cataloging all the proteins in saliva. It is packed with proteins that help manage the teeming hordes of microbes in our mouths. It is stuffed with materials that make us spit gristly, end our enamel from dissolving and assist heal wounds. It is packed with a plethora of hormones and other chemicals revealing something from whether one smokes to whether one is stressed. Saliva is a clear, slightly acidic mucoserous exocrine Biofluid produced in the oral cavity by means of three predominant (parotid, submandibular and sublingual). It is a complicated fluid containing an complete library of hormones, proteins, enzymes, antibodies, antimicrobial constituents, and
DYNAMIC ANALYSIS OF ROTOR SYSTEM WITH ACTIVE MAGNETIC BEARINGS USING FINITE ELEMENT METHOD

The rotor is a key element of rotating machinery that can be used in different of mechanical devices such as gas turbines, industrial compressors, motors, and aero engines. The dynamic analysis of a rotor system with active magnetic bearings is presented in this paper. This system consists of a flexible shaft with a rigid disk and flexible bearings. An sys Workbench is used to study a 3D simulation of a finite element model. Correct estimation of the rotor's natural frequencies and mode shapes is required for rotating machinery safety procedures. In order to compute the natural frequencies and mode shapes of this system, in the first case it is assumed a free-free system in which no bearings are used, and the natural frequencies and mode shapes are calculated without rigid body modes. In the second case, the natural frequencies and mode shapes are determined with bearing stiffness. The natural frequencies are also evaluated using finite elements based on matlab code. A comparison between the two methods was carried out.
MICROALGAL BIOMASS PRODUCTION AND OIL EXTRACTION FOR SUSTAINABLE BIODIESEL PRODUCTION

Knowing the limitations caused by fossil fuels various researches are being conducted in search of a substitute fuel. One such substitute fuels are biodiesel. A microalgal feedstock is considered as the ideal third generation biodiesel as they are a highly efficient and sustainable source of fuel due to their high accumulation of lipid and are cultured without agricultural land or ecological landscapes, they help withglobal warming and treatment of wastewater. Even though they provide us with all these benefits, they pose various challenges. This review paper focuses on the important aspects of the production of biodiesel and addresses the challenges posed in the process. We discuss the process in detail from the efficient way to select the microalgal strain, effects of biotechnology and genetic engineering in improving the efficiency of the microalgae, the most efficient culture system and bioreactor used, and the optimum conditions that help the accumulation of the most amount of oil, detailed harvesting methods where both the physical, chemical, and biological methods are discussed in detail. The details of oil conversion to biodiesel by transesterification method followed by the recent developments to increase the oil production by the addition of nano-particles, advantages, and prospects have been discussed.
Portal Diagnosis and prognosis of Alzheimer s disease in subjects with mild cognitive impairment

Objective To compare the predictive accuracy of beta amyloid (Aβ)1-42 and total tau in cerebrospinal fluid (CSF), hippocampal volume (HCV), and apolipoprotein E (APOE) genotype for AD-type dementia in subjects with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). Methods We selected 399 subjects with aMCI and 226 subjects with naMCI from a multicenter memory clinic-based cohort. We measured CSF Aβ1-42 and tau by ELISA (n=231), HCV on MRI (n=388), and APOE-ε4 (n=523). Follow-up was performed annually up to 5 years. Outcome measures were progression to AD-type dementia and cognitive decline. Results At least 1 follow-up was available for 538 subjects (86%). 132 (38%) subjects with aMCI and 39 (20%) subjects with naMCI progressed to AD-type dementia after an average follow-up of 2.5 years. CSF Aβ1-42, tau and Aβ1-42/tau ratio, HCV, and APOE-ε4 predicted AD-type dementia in each MCI subgroup with the same overall diagnostic accuracy. However, CSF Aβ1-42 concentration was higher and hippocampal atrophy less severe in subjects with naMCI compared to aMCI. This reduced the sensitivity but increased the specificity of these markers for AD-type dementia in subjects with naMCI. Conclusions AD biomarkers are useful to predict AD-type dementia in subjects with aMCI and naMCI. Biomarkers may, however, not be as sensitive for early diagnosis of AD in naMCI compared to aMCI. This may have implications for clinical implementation of the NIA-AA criteria.
P SYCHOLOGICAL Q UALITY AND M ENTAL A DJUSTMENT OF E NTERPRISE M ANAGERS

The ability of mental adjustment of managers may influence the sustainable development of the enterprise. This paper fully explores the psychological quality and mental adjustment of enterprise managers. Firstly, the relevant theories on enterprise managers and psychology were fully reviewed. Then, a questionnaire survey was conducted on 300 managers, in which the psychological quality of each respondent was assessed against a five-point scale. The psychological health of the respondents was compared based on gender, length of service, education, position and business scale. In addition, the authors analysed the relationship between work stress and psychological quality, and checked the managers’ cognition of mental health and response to work stress. The results show that psychological quality of enterprise managers is generally poor and varies from person to person, and the enterprise managers lack the knowledge on psychological quality. On this basis, mental adjustment strategies were proposed for enterprises managers in the enterprise, social and individual dimensions. The research results provide a good reference for human resources management.
A Survey on AI and Ethics: Key factors in building AI trust and awareness across European citizens.

Artificial Intelligence (AI) is a key field that has attracted globally huge interest of businesses, policymakers, and the public in the last years. This includes the European Union which has adopted a human-centred approach to foster AI in Europe with respect to human rights and key European values. Consider, for example, the seven principles presented in the Ethics Guidelines for Trustworthy AI [1] by the AI High-Level Expert Group. Since its release, the European Commission (EC) set the expectation to develop, through a series of actions, a European AI more beneficial and secure for all citizens. The AI4EU Observatory on Society and AI (funded by an Horizon 2020 project) created different Working Groups (WG) to undertake a research on how European guidance has been applied in concerning the Ethical, Legal, Socio-Economic and Cultural issues on AI (ELSEC AI). In particular, the WG on Social Awareness aimed to understand the public perceptions about AI (e.g. attitude, awareness and trust) and the factors that may help in boosting trust in AI in Europe. The survey was designed based on a literature review on previous consultations focused on public opinion on AI. This included surveys as “Public view of Machine Learning by the Royal Society” [3], the European Consultation on AI by the Atomium European Institute (ECAI) [4], the Moral Machine platform by Scalable Cooperation and the MIT Media Lab [5] and the “Trust in Artificial Intelligence: Australian Insights” by KPMG and the University of Queensland [6]. Some of the questionnaires were publicly available, such as the one by the Royal Society, while others were not fully accessible as the proposed consultation by ECAI. With our review, we highlight how a survey on AI and its social impact at a European level was still missing and that our contribution to this topic might be relevant to fill the existing gap. The survey gathered the opinion of 4000 European citizens from Italy, Spain, France, Germany, The Netherlands, Sweden, Poland, Romania. For each country a representative sample of 500 people was selected based on gender, age (spanning from 18 to 75) and geographic areas. In our questionnaire we investigate three key factors: (i) AI Awareness (7 items), (ii) AI Attitude (5 items) and (ii) Trust in AI (4 items). The first category, AI Awareness refers to a set of questions analysing people's self-reported knowledge of AI and understanding of its impact on their life. AI attitude focuses on the citizen’ approach toward AI and their interest to improve their current knowledge on the subject. Lastly, Trust in AI assesses the trust of citizens towards AI and possible entities to ensure its correct use. The collection of the data and the process of the results has been done in collaboration with Marketing Problem Solving (MPS) through a Computer-assisted web interviewing (CAWI). The full release of the data and the final report will be publicly shared soon. An initial analysis will be presented during the workshop based on the following results:
Comparative Study on Lateral Load Resisting System in Tall Building

In this enchanting world of structure design, building with new innovative ideas most of the time it is with the sky touching height. As these structures are extended vertically they are going to with stand the lateral loads in an enormous intensity. In this paper an investigation has been carried out over a 40 storey RCC structural space frame using different lateral load resisting system used in tall buildings. These structural systems used in the study are “shear wall”, “outrigger” and “dia-grid”. Analysis has been carried out using different method of analysis for static earthquake forces, dynamic earthquake forces (response spectrum analysis and time history analysis performed under the guidelines of IS: 1893-2002 and IS: 800-2000 respectively) and static wind forces. This will help to understand the behaviour of the mentioned lateral load resisting systems under dynamic load effect. The basic modelling technique and assumption are made by using ETABS 15.2. 0 and other consideration are made according to the Indian Standard. A comparison of the top storey displacement, storey drift of the frame and the time period of the whole structure is done for different configuration of lateral load resisting systems. At the completion of the study the conclusion will be arrived and stated regarding to the effect of seismic load application.
nderstanding the Structure of Social Inequities in Childhood besity

a n o s he challenges of addressing childhood obesity include the need to close the gap in obesity rates between children and adolescents of ethnic inority and/or lower socioeconomic status (SES) ackgrounds and their white or more affluent counterarts. In low-income, urban, African-American and atino communities, rates of overweight or obesity using the Centers for Disease Control and Prevention 5th percentile cut-off) surpassing 40% of elementary chool populations have been documented. As noted n the Institute of Medicine (IOM) report assessing rogress in childhood obesity prevention, some risk actors are relatively ubiquitous across all settings, but ost are more concentrated in low-income communiies of color. This excess risk produces staggering isparities in obesity-related co-morbidities. For examle, the lifetime odds of developing type 2 diabetes for atinos or African Americans are nearly one in two at irth, twice that of white newborns. Consequently, ddressing the inequities underlying these disparities as become a priority within the Robert Wood Johnson oundation’s (RWJF) focus on childhood obesity. heir national research programs, such as Bridging the ap, elucidate important links to needed solutions. Several papers in this supplement to the American ournal of Preventive Medicine point to widespread envionmental attributes that may be “obesogenic” for ow-income and/or ethnic minority youth. The proporion of high-calorie–low-nutrient–density school food hoices and fast-food restaurants was found to be igher in communities with higher poverty rates, lower ousehold median incomes, and higher concentraions of ethnic minority residents. Exposure to foodelated television advertising was found to be 60% reater among African-American children, with fast ood as the most frequent category, because of both argeted marketing and higher TV viewing rates. In
Maintenance of business machines with edge and cloud collaboration

Recently, internet of things (IoT) technologies have progressed, but IoT maintenance applications are not widespread in Japan yet because of insufficient analyses of real‐time situations and the high costs of configuring failure detection rules and collecting sensing data. In this paper, using lambda architecture concept, we propose a maintenance platform on which edge nodes analyze sensing data, detect anomalies and extract a new detection rule in real time, and a cloud orders maintenance automatically. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
The Effect of Information and Voter Confidence on Turnout

Voter turnout is an important factor when attempting to assess a population’s preferences accurately. Understanding the factors that influence voter turnout will aid in raising electoral participation. This paper focuses on the effect of informed voters and voter confidence on voter turnout. It is concluded that informed voters are strongly linked with higher voter turnouts. Voter confidence is decomposed into voter confidence (perception of electoral integrity) and voter self-confidence (perception of the “correct” decision being made). It is concluded that increased voter confidence is linked to higher voter turnout. It is foreshadowed based on research outside the political domain that voter self-confidence would show similar results if tested.
Sphere Mobile Robots for Highly Connected Wireless Multihop Sensor Networks

—For wireless sensor networks with autonomously mobile sensor nodes, the sensor nodes are required to support both agile mobility and high connectivity. The agile mobility guarantees high observability and the high connectivity realizes high performance wireless multihop transmissions of sensor data. This paper proposes sphere mobile robots with sensor devices which moves any direction without the time overhead for change of mobility direction and with multiple directional antennas to detect mobility of neighbor mobile robots. Based on the change of direction and receipt power of wireless signals from the neighbor mobile robots, each mobile robots ﬁnd its way to keep connectivity with the neighbor mobile robots. The constitution of the sphere mobile robots and the results of the brief performance evaluation experiments are also discussed.
Non-Linear Modeling and Control of Unmanned Air Vehicle

A Master of Science thesis in Mechatronics Engineering by Roozbeh Falah Ramezani entitled, "Non-Linear Modeling and Control of Unmanned Air Vehicle," submitted in May 2012. Thesis advisor is Dr. Ali Jhemi. Available are both soft and hard copies of the thesis.
Epidemiology of Paediatric Out-of-Hospital Cardiac Arrest Presented to a Local Emergency Department: A Retrospective Case Series

Objective To outline the epidemiology of paediatric out-of-hospital cardiac arrest (OHCA) cases presented to a regional hospital. Design Retrospective case series. Methods Attendance records of critical cases aged from 0 to 17 years old from year 2001 to 2014 attending Emergency Department (ED) of Yan Chai Hospital were searched. Demographic data and predictive parameters were analysed for association with outcome. Results There were 40 cases of paediatric OHCA throughout the study period. A total of 21 (52.5%) had witnessed arrest; 27 (67.5%) had cardiac arrest at home. Twenty-eight (70%) of the cardiac arrest were first noted by family members; 8 (20%) received bystander cardiopulmonary resuscitation (CPR). Only 13 (32.5%) cases had return of spontaneous circulation (ROSC) in the ED and were admitted. Seven patients (17.5%) survived to hospital discharge. Conclusion Paediatric OHCA, although a rare occurrence, has a high mortality of 82.5% in our series. Majority of the cases occur at home, and are witnessed. Family members are often the first persons to detect the arrest. Yet the rate of providing bystander CPR is low in Hong Kong.
Parameter Analysis and Optimization of the Rotating Arc NG-GMAW Welding Process

Parameters of the narrow gap gas metal arc welding (NG-GMAW) method are complex and strongly coupled, which can easily cause welding defects, such as poor sidewall fusion, overlap, gas pore and slag inclusion. So parameter optimization of NG-GMAW is very difficult. The common experimental analysis method is complex, especially take a long time and cost. In this paper, the simulation analysis method is used to analyse NG-GMAW welding process. After the analysis of the NG-GMAW welding mechanism, the dynamic simulation model of the rotating arc NG-GMAW welding process is established firstly. Then, the influence of key process parameters on the welding quality is analysed in detail, which include the rotation angular velocity, rotation angular amplitude, wire feed speed, welding speed, sidewall stay time and so on. The simulation and analysis results illustrate the role and influence of the above parameters. In short, this paper provides a flexible and efficient method for the analysis and optimization of NG-GMAW welding process parameters to discover new phenomena, improve efficiency and save cost.
Precise Quantification of Molybdate In Vitro by the FRET-Based Nanosensor ‘MolyProbe’

Molybdenum (Mo) is an essential trace element in all kingdoms of life. Mo is bioavailable as the oxyanion molybdate and gains biological activity in eukaryotes when bound to molybdopterin, forming the molybdenum cofactor. The imbalance of molybdate homeostasis results in growth deficiencies or toxic symptoms within plants, fungi and animals. Recently, fluorescence resonance energy transfer (FRET) methods have emerged, monitoring cellular and subcellular molybdate distribution dynamics using a genetically encoded molybdate-specific FRET nanosensor, named MolyProbe. Here, we show that the MolyProbe system is a fast and reliable in vitro assay for quantitative molybdate determination. We added a Strep-TagII affinity tag to the MolyProbe protein for quick and easy purification. This MolyProbe is highly stable, resistant to freezing and can be stored for several weeks at 4 °C. Furthermore, the molybdate sensitivity of the assay peaked at low nM levels. Additionally, The MolyProbe was applied in vitro for quantitative molybdate determination in cell extracts of the plant Arabidopsis thaliana, the fungus Neurospora crassa and the yeast Saccharomyces cerevisiae. Our results show the functionality of the Arabidopsis thaliana molybdate transporter MOT1.1 and indicate that FRET-based molybdate detection is an excellent tool for measuring bioavailable Mo.
Credit Card Fraud Detection with Automated Machine Learning Systems

ABSTRACT The steady increase at the turnover of online trading during the last decade and the increasing use of credit cards has subsequently made credit card frauds more prevalent. Machine Learning (ML) models are among the most prominent techniques in detecting illicit transactions. In this paper, we apply the Just-Add-Data (JAD), a system that automates the selection of Machine Learning algorithms, the tuning of their hyper-parameter values, and the estimation of performance in detecting fraudulent transactions using a highly unbalanced dataset, swiftly providing prediction model for credit card fraud detection. The training of the model does not require the user setting up any of the methods’ (hyper)parameters. In addition, it is trivial to retrain the model with the arrival of new data, to visualize, interpret, and share the results at all management levels within a credit card organization, as well as to apply the model. The model selected by JAD identifies 32 out of a total of 39 fraudulent transactions of the test sample, with all missed fraudulent transactions being small transactions below 50€. The comparison with other methods on the same dataset reveals that all the above come with a high forecasting performance that matches the existing literature.
Eco-Friendly Synthesis of PEtOz-PA: A Promising Polymer for the Formulation of Curcumin-Loaded Micelles

The need to develop alternative methods or to use “green” solvents constitutes an essential strategy under the emerging field of green chemistry, particularly in the development of new synthetic strategies in the field of pharmaceutic industry. We report an eco-friendly method of synthesis of poly(2-ethyl-2-oxazoline)-palmitoylate (PEtOz-PA) using Er(OTf)3 as Lewis’s acid catalyst in 2-MeTHF. The novel biomolecule derivative was characterized to confirm palmitoyl group substitution and employed for the formulation, characterization, and antioxidant activity evaluation of curcumin-loaded polymeric micelles.
Real-Time Automated Socket Inspection using Advanced Computer Vision and Machine Learning : DI: Defect Inspection and Reduction

Our test tools pick and place units into sockets for electrical testing. Defects or loose debris accumulated inside the test sockets will likely damage each subsequent unit being tested until the issue is detected and the defective socket is repaired or replaced. To resolve this critical issue, we equipped each pick-and-place arm with a new machine vision system designed to fit inside the existing tool. The limited footprint constraints required a highly compact imaging system which resulted in a variety of image artifacts, creating several unique challenges for the inspection system. We developed an inspection algorithm that utilizes a variety of advanced computer vision and machine learning techniques to normalize and match the images, remove artifacts, and detect defects. The flagged socket images can be manually dispositioned by the user and the socket can be sent for repair or cleaning as needed.