Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Revolution in Periodontology

Change towards the digital era is an irreversible global trend. With technology developing at a faster pace, the Digital Revolution has now fully entered the world of dentistry and has become more user-friendly allowing dental professionals to work in smarter ways than before. Diagnostic precision reduces errors and 3D planning for therapies opens the way toward a novel, minimally invasive dentistry that uses compatible and aesthetic materials. Virtual planning ensures predictable aesthetic and functional rehabilitation, painless postoperative recovery, and better communication with patients, thus meeting their expectations. Digital techniques are always superior and will surely become the future of dentistry. So, the need of the hour is to incorporate more digitization into our practice for the comfort of both dentist as well as the patient. The purpose of this article is to explore around the application of digital dentistry in diagnosis and treatment of periodontal disease.
Forecasting Financial Stress and Economic Sensitivity in CEE Countries

This paper presents forecasts for the Financial Stress Index (FSI) and the Economic Sensitivity Index (ESI) for the period 2014-2015 for six countries in the region, namely the Czech Republic, Estonia, Hungary, Latvia, Lithuania and Poland. It is a continuation of the endeavor to construct synthetic indices measuring financial stress and economic sensitivity for twelve Central and East European countries using the Principal Component Analysis. In order to obtain forecasts of the FSI, we estimated Vector Autoregression (VAR) models on monthly data for the period 2001-2012 separately for all the countries. Using quarterly historical values of ESI and FSI, we estimated Dynamic Panel Data Model for the complete sample of countries. Parameters of the model were later used for forecasting the ESI. Obtained results suggest that the FSI will start to rise in 2014 in the Czech Republic, Lithuania, and Estonia. For Latvia and Hungary, we observed a conversion in the trend, i.e. at the beginning of 2015, when the index should start to fall. According to our forecasts, the ESI will be rising in the next two years, except for Hungary, where we predict a continuous decrease in economic sensitivity.
The African Journal of Information Systems The African

Identity disclosure risk is an essential consideration in data anonymization aimed at preserving privacy and utility. The risk is regionally dependent. Therefore, there is a need for a regional empirical approach in addition to a theoretical approach in modeling disclosure risk estimation. Reviewed literature pointed to three influencers of the risk. However, we did not find literature on the combined effects of the three influencers and their predictive power. To fill the gap, this study modeled the risk estimation predicated on the combined effect of the three predictors using the Kenyan situation. The study validated the model by conducting an actual re-identification quasi-experiment. The adversary’s analytical competence, distinguishing power of the anonymized datasets, and linkage mapping of the identified datasets are presented as the predictors of the risk estimation. For each predictor, manifest variables are presented. Our presented model extends previous models and is capable of producing a realistic risk estimation.
There Is Always a Way Out! Destruction-Resistant Key Management: Formal Definition and Practical Instantiation

A central advantage of deploying cryptosystems is that the security of large high-sensitive data sets can be reduced to the security of a very small key. The most popular way to manage keys is to use a ( t , n ) − threshold secret sharing scheme: a user splits her/his key into n shares, distributes them among n key servers, and can recover the key with the aid of any t of them. However, it is vulnerable to device destruction: if all key servers and user’s devices break down, the key will be permanently lost. We propose a Destruction-Resistant Key Management scheme, dubbed DRKM, which ensures the key availability even if destruction occurs. In DRKM, a user utilizes her/his n ∗ personal identification factors (PIFs) to derive a cryptographic key but can retrieve the key using any t ∗ of the n ∗ PIFs. As most PIFs can be retrieved by the user per se without requiring stateful devices, destruction resistance is achieved. With the integration of a ( t , n ) − threshold secret sharing scheme, DRKM also provides portable key access for the user (with the aid of any t of n key servers) before destruction occurs. DRKM can be utilized to construct a destruction-resistant cryptosystem (DRC) in tandem with any backup system. We formally prove the security of DRKM, implement a DRKM prototype, and conduct a comprehensive performance evaluation to demonstrate its high efficiency. We further utilize Cramer’s Rule to reduce the required buffer to retrieve a key from 25 MB to 40 KB (for 256-bit security).
The effect of using simulation strategy on preparatory school students' earthquakes preparedness

--- Background: Earthquake disaster preparedness in schools is an effective, adaptable, and long-lasting approach to disseminating disaster education. Undoubtedly, systematic, quantifiable, and effective measures to increase the capacity of school community members will lessen the impact of catastrophe risk in schools. Aim: To evaluate the effect of using a simulation strategy on preparatory school students' earthquake preparedness. Design: A quasi-experimental design pre-and post-tests were utilized. Setting: The
Regional neuroin�ammation caused by peripheral infection is involved in fatigued symptoms: A [18F]DPA-714 PET study

A series of symptoms, including fever, widespread pain, fatigue, and even ageusia, have frequently been reported in the context of various infections, such as COVID-19. Although the pathogenic mechanisms underlying an infection causing fever and pain have been well established, the mechanisms of fatigue induced by infection remain unclear. To elucidate whether and how the peripheral infection cause fatigue via regional neuroin�ammation, we performed a brain-wide investigation of neuroin�ammation in a peripheral pseudoinfection rat model using [ 18 F]DPA-714 positron emission tomography (PET) imaging analysis, in which the polyriboinosinic: polyribocytidylic acid (poly I:C) was intraperitoneally injected. Consistent with previous reports, transient fever lasting for several hours and subsequent suppression of spontaneous activity lasting a few days were induced by poly I:C treatment. Signi�cant increase in plasma interleukin (IL)-1 β , IL-6 and tumour necrosis factor (TNF)- α were observed at 2 and 4 h following poly I:C treatment. PET imaging analysis revealed that the brain uptake of [ 18 F]DPA-714 was signi�cantly increased in several brain regions one day after poly I:C treatment, such as the dorsal raphe (DR), parvicellular part of red nucleus (RPC), A5 and A7 noradrenergic nucleus, compared with the control group. The accumulation of [ 18 F]DPA-714 in the DR, RPC and A5 was positively correlated with the fatigue-like behavior, and that in the A7 tended to positively correlate with fever. These �ndings suggest that peripheral infection may trigger regional neuroin�ammation, which may cause speci�c symptoms such as fatigue. A similar mechanism might be involved in COVID-19.
Using Natural Language Processing (Nlp) And Deep Learning To Analyze Sentiment In Memes

Social media is the most popular interactive platform which is most common among people, today memes are a hot topic. Originally memes originated to have funny content but due to later development, it rather can be offensive sometimes, usually some hateful message or character image. Some memes may have abusive content and have the wrong impact on our society. It‘s difficult to categorize such memes with Human interaction This paper presents an approach to analyze and detect sentiment in memes using Natural Language Processing (NLP) and Deep Learning. It will detect offensive memes, in three steps. First, it will extract the text from the given image, then it will classify the given text as offensive or not offensive. If the text is found to be offensive then in the third step it will further classify offensive text in three categories namely slight offensive, very offensive, and hateful offensive. The model uses very simple architecture with a multi-layer dense network structure involving NLP with RNN and LSTM along with word embeddings such as GloVe and fast text.
The Effect of Regulatory Sandbox on The Behavior of FinTech Actors in Indonesia Using Theory of Planned Behavior Approach

The growth of FinTech has become a new phenomenon in Indonesia. Though, Indonesia’s Government has implemented regulation to facilitate the development of digital financial innovations known as Regulatory Sandbox. Still, the ratio of legal FinTech is very small compared to illegal FinTech. This research using quantitative methods by building 8 hypotheses through the Theory of Planned Behavior approach. The primary data is based on 107 respondent’s, resulted 6 hypotheses have a positive effect while 2 hypotheses have no effect. The findings indicate that one of the reasons why illegal FinTech is greater than legal FinTech is due to lack of interest from FinTech Actors whereas the factor that reduces this interest is the low influence of the authorities and stakeholders in making the Regulatory Sandbox a top priority. This research provides benefits for all stakeholders to make the necessary improvements to increase interest in participating in the Regulatory Sandbox.
REDUCING THE L-H TRANSITION POWER THRESHOLD IN DIII-D ITER-SIMILAR-SHAPE HYDROGEN PLASMAS

Recent dedicated DIII-D experiments in low-torque, ITER-similar-shape (ISS) hydrogen plasmas ( q 95 ~3.6) show that the L-H transition power threshold P LH can be reduced substantially (~ 30%) with moderate helium trace admixtures (n He /n e ≤ 25%). These plasmas are electron-heat dominated, with temperatures T e (0)/ T i (0) ≥ 1 and edge heat flux ratio Q e / Q i ( ρ = 0.95) ~1.2-1.5. Compared to reference ISS plasmas with q 95 ~3.6, ISS hydrogen plasmas with increased q 95 ~5.1 also exhibit a substantially reduced power threshold, not accounted for by the commonly used multi-machine threshold scaling [1]. Techniques for reducing P LH are very important for ITER, in particular for hydrogen plasma operations during the Pre-Fusion Power Operation-1 (PFPO-1) campaign with marginal auxiliary heating power (20-30 MW of ECH). In experiments with unfavorable grad-B direction, the increased L-H transition power threshold is clearly attributed to reduced E × B flow shear near the last closed flux surface due to higher edge toroidal co-rotation. However, the L-H transition power threshold in ISS plasmas with unfavorable ion ∇ B drift reduces to values similar to those observed in favorable ion ∇ B configuration via a fast, localized reduction or reversal of edge toroidal (carbon) (co)rotation ( ρ ~ 0.94). This reversal/reduction of intrinsic edge co-rotation appears to be triggered by transient increases in edge power flow during sawteeth. The threshold reduction occurs over a range in NBI torque but is most effective for balanced torque or with low counter-current torque.
A contribution to the natural distribution of Aesculus hippocastanum (Hippocastanaceae) in Greece

Horse chestnut (Aesculus hippocastanum) is an endemic species to Greece. The information on its natural distribution in Greece is limited. After a destructive primary pest of horse chestnut, the leaf miner Cameraria ohridella, has spread in recent years almost all over Europe, much attention has been focused on the natural gene pool of the host. In a study of its distribution in Greece during the period 2001-2004, we found 37 new scattered locations of natural trees, in small or large clusters and in mixed stands with beech (Fagus sylvatica), dominating and covering an area of 750 ha. Horse chestnut in Greece shows remarkable habitat adaptability, as it occurs in a wide range of altitudes from 228 m to 1485 m a.s. l.
Mechanisms of Activation of Brain’s Drainage during Sleep: The Nightlife of Astrocytes

The study of functions, mechanisms of generation, and pathways of movement of cerebral fluids has a long history, but the last decade has been especially productive. The proposed glymphatic hypothesis, which suggests a mechanism of the brain waste removal system (BWRS), caused an active discussion on both the criticism of some of the perspectives and our intensive study of new experimental facts. It was especially found that the intensity of the metabolite clearance changes significantly during the transition between sleep and wakefulness. Interestingly, at the cellular level, a number of aspects of this problem have been focused on, such as astrocytes–glial cells, which, over the past two decades, have been recognized as equal partners of neurons and perform many important functions. In particular, an important role was assigned to astrocytes within the framework of the glymphatic hypothesis. In this review, we return to the “astrocytocentric” view of the BWRS function and the explanation of its activation during sleep from the viewpoint of new findings over the last decade. Our main conclusion is that the BWRS’s action may be analyzed both at the systemic (whole-brain) and at the local (cellular) level. The local level means here that the neuro-glial-vascular unit can also be regarded as the smallest functional unit of sleep, and therefore, the smallest functional unit of the BWRS.
PART OF A SPECIAL ISSUE ON HALOPHYTES AND SALINE ADAPTATIONS Changes in the alternative electron sinks and antioxidant defence in chloroplasts of the extreme halophyte Eutrema parvulum (Thellungiella parvula) under salinity

† Background and AimsEutremaparvulum (synonym,Thellungiellaparvula) is an extreme halophyte that thrives in high salt concentrations (100–150 mM) and is closely related toArabidopsisthaliana. The main aim of this study was to determine how E. parvulum uses reactive oxygen species (ROS) production, antioxidant systems and redox regulation of the electron transport system in chloroplasts to tolerate salinity. † Methods Plants of E. parvulum were grown for 30 d and then treated with either 50, 200 or 300 mM NaCl. Physiological parameters including growth and water relationships were measured. Activities of antioxidant enzymes were determined in whole leaves and chloroplasts. In addition, expressions of chloroplastic redox components such as ferrodoxin thioredoxin reductases (FTR), NADPH thioredoxin reductases (NTRC), thioredoxins (TRXs) and peroxiredoxins (PRXs), as well as genes encoding enzymes of the water –water cycle and proline biosynthesis were measured. † Key Results Salt treatment affected water relationships negatively and the accumulation of proline was increased by salinity. E. parvulum was able to tolerate 300 mM NaCl over long periods, as evidenced by H2O2 content and lipid peroxidation. While Ca 2+ and K + concentrations were decreased by salinity, Na + and Cl – concentrations increased. Efficient induction of activities and expressions of water–water cycle enzymes might prevent accumulation of excess ROS in chloroplasts and therefore protect the photosynthetic machinery in E. parvulum. The redox homeostasis in chloroplasts might be achieved by efficient induction of expressions of redox regulatory enzymes such as FTR, NTRC, TRXs and PRXs under salinity. † ConclusionsE.parvulum was able to adapt to osmotic stress by an efficient osmotic adjustment mechanism involving proline and was able to regulate its ion homeostasis. In addition, efficient induction of water –water cycle enzymes and other redox regulatory components such as TRXs and PRXs in chloroplasts were able to protect the chloroplasts from salinity-induced oxidative stress.
Short-term traffic forecasting model: prevailing trends and guidelines

The design parameters serve as an inte gr al part of developing a robust short-term traffic forecasting model. These parameters include scope determination, input data pr e par ation, output par ameters and modelling techniques. This paper takes a further leap to analyse the r ecent tr end of design parameters thr ough a systematic literatur e r e vie w based on peer-re vie wed articles up to 2021. The ke y important findings are summarized along with the challenges of performing short-term traffic for ecasting. Intuiti v el y, this paper offers insights into the next w av e of resear c h that contributes significantly to industries.
Metabolomic Profiling of Second-Trimester Amniotic Fluid for Predicting Preterm Delivery: Insights from NMR Analysis

Preterm delivery (PTD) is a notable pregnancy complication, affecting one out of every ten births. This study set out to investigate whether analyzing the metabolic composition of amniotic fluid (AF) collected from pregnant women during the second trimester of pregnancy could offer valuable insights into prematurity. The research employed 1H–NMR metabolomics to examine AF samples obtained from 17 women who gave birth prematurely (between 29+0 and 36+5 weeks of gestation) and 43 women who delivered at full term. The application of multivariate analysis revealed metabolites (dimethylglycine, glucose, myo-inositol, and succinate) that can serve as possible biomarkers for the prognosis and early diagnosis of preterm delivery. Additionally, pathway analysis unveiled the most critical metabolic pathways relevant to our research hypothesis. In summary, these findings suggest that the metabolic composition of AF in the second trimester can be a potential indicator for identifying biomarkers associated with the risk of PTD.
A STUDY ON THE IMPACTS OF EXPORT AND IMPORT ON GHANA’S ECONOMIC GROWTH

: The research studies the impact on global trade import and export on Ghana’s economy using import, export and foreign direct investment as dependent variables and gross domestic product as independent variable. The study used ordinary least square regression as the model of analysis using Augmented Dickey fuller method to determine if the variables have unit root thus determining if the variable are stationary or not stationary at level. The unit root test detected that three variables were stationary after first difference that is import, foreign direct investment and gross domestic product while export was differenced twice and became stationary.The research recommends the government of Ghana to focus on promoting an export-led economy. The government should make good use of its resource abundance in cocoa, manganese, oil, timber, gold etc. Various production sites can be opened to aid in processing of natural resources within the country and also new policy “one district one factory” implemented should be regulated effectively to curtail the problem of inter dependency. This will boost the country’s economy as well as promoting trade facilitation in the long run.
Detection and Implementation of Indian Currencies Based on Computer Vision Approach

: Currency recognition is a simple process of identifying the denominational value of a currency. But currency detection is a complicated task concerning machines. Currency has intrinsic as well as extrinsic properties. In this method preprocessing steps like edge detection, character extraction will be involved. After extracting the feature, pattern recognition technique is used to find the value of money. And based on the algorithm developed the currencies were correctly classified with a success rate of approximately 97%. The processing time taken is 2.52 seconds. Fourier Descriptors are used for feature extraction of unique I
Aviation Hazards in the Sky over Thada as Revealed by Meso-scale Meteorological Modeling

The airplane of Nepal Army in-route to Kathmandu Airport from Nepalgunj lost its communication with the air traffic control room from the sky of Thada of Arghakhachi District. The plane was found with breath taking crashing over the Bowang high mountain area of Baglung District. The weather pattern over the area has been numerically reconstructed with the application of Weather Research and Forecasting (WRF) model initialized with NCEP FNL meteorological and USGS land use and terrain elevation data to examined the effect of prevailing weather pattern. The reconstructed weather pattern show that the atmosphere over Thada and associated areas capture an adverse condition for aviation activities, as it appears highly turbulent and is intense subsidence prone. A detail long-term investigation usefully complemented with field observation may provide better understanding and for improved flight safety. Journal of Institute of Science and Technology, 2014, 19(2): 65-70
For affected breast cancer Patients undergoing a breast-conserving surgical operation, a nursing approach is primarily based on the Roy Adaptation Model

: Nursing models allow nurses attention to nursing work and its applications relatively more than medical practice. Furthermore, it promotes systematic, purposeful, controlled, and patient-satisfaction satisfaction care. Roy Adaptation Model is one of the most widely used models in nursing. Nursing's goal, according to the Roy adaptation model, is to increase regulatory and life expectancy. The Roy Adaptation Model assesses the patient in four modes: physiologic, self-concept, role function, and interdependence, with the goal of providing holistic care. This article describes the application of Roy’s adaptation Model in the treatment of a patient who was diagnosed with breast cancer and underwent breast-conserving surgery. The Appropriate theoretical model's four methods (physiologic, self-concept, and behavioral) were used to evaluate patient data.
Genome-Wide Mutant Fitness Proﬁling Identiﬁes Nutritional Requirements for Optimal Growth of Yersinia pestis in Deep Tissue

Rapid growth in deep tissue is essential to the high virulence of Yersinia pestis , causative agent of plague. To better understand the mechanisms underlying this unusual ability, we used transposon mutagenesis and high-throughput sequencing (Tn-seq) to systematically probe the Y. pestis genome for elements contributing to ﬁtness during infection. More than a million independent insertion mutants representing nearly 200,000 unique genotypes were generated in fully virulent Y. pestis . Each mutant in the library was assayed for its ability to proliferate in vitro on rich medium and in mice following intravenous injection. Virtually all genes previously established to contribute to virulence following intravenous infection showed signiﬁcant ﬁt-ness defects, with the exception of genes for yersiniabactin biosynthesis, which were masked by strong intercellular complementation effects. We also identiﬁed more than 30 genes with roles in nutrient acquisition and metabolism as experiencing strong selection during infection. Many of these genes had not previously been implicated in Y. pestis virulence. We further examined the ﬁtness defects of strains carrying mutations in two such genes—encoding a branched-chain amino acid importer ( brnQ ) and a glucose importer ( ptsG )—both in vivo and in a novel deﬁned synthetic growth medium with nutrient concentrations matching those in serum. Our ﬁndings suggest that diverse nutrient limitations in deep tissue play a more important role in controlling bacterial infection than has heretofore been appreciated. Because much is known about Y. pestis pathogenesis, this study also serves as a test case that assesses the ability of Tn-seq to detect virulence genes.