Miracle-dz/UniFAR-FTUs huggingface dataset:
Topography alteration of different implant surfaces after an installation

Objective: The aims of present study were to investigate dental implant surface alterations after the installation. Materials and methods: Four types of implant with different surface modification (sandblasted and acid-etched (SLA), SLA with nano-apatite coating, laser, and anodized surface) were assigned to control and test groups (n=5). The installation was performed in porcine bone. Consequently, the implant surfaces were investigated with scanning electron microscope and confocal laser microscope to identify micro topographical change. Results: Morphological alterations were found in all test implants such as thread abrasion, flattening area, crack, and decreased surface projection. Topographical analysis showed that arithmetical mean height and surface area significantly decreased in all test groups (2-20% and 3-6%, respectively). Surface skewness of SLA significantly increased after installation. Surface kurtosis and texture aspect ratio remained the same. Conclusions: All implant surfaces exhibited the surface alteration after the installation. The anodized implant was the least change. SLA implant presented the greatest surface change after the installation. Vijintanawan S, Cahiyasamut T, Wongsirichat 40: 95-106.
Sustainable multi-products delivery routing network design for two-echelon supplier selection problem in B2B e-commerce platform

This paper examines the environmental impact produced by multi-vehicle transportation on a sustainable supply chain (SC) network. The relevance of green principles is gaining momentum day by day, which has forced the governments to introduce carbon emission schemes for the transportation associated with the firms. Various countries around the globe are introducing carbon-pricing schemes, in which a carbon tax is imposed based on the amount of anthropogenic emissions. A firm, which sets environmental standards for the emission associated with its operational activities, should design a transportation network based on the trade-off between its economic efficiency and the carbon emission. In this paper, the main focus is to design a sustainable supply chain network. A mixed-integer-non-linear-programming (MINLP) model is formulated to minimize the overall cost incurred in a multi-vehicle, multi-product sustainable transportation network. The meta-heuristic approach i.e., Hybrid Chemical Reaction Optimization Algorithm with Tabu search (CRO-TS) and LINGO solver have been used to solve the proposed model. This analysis can guide the government to encourage the logistics service providers to capitalize on anthropogenic gas emission systems and simultaneously design the tax policy on carbon emission.
Project Studies on environmental co-factors potentially influencing the disease dynamics of Florida’s coral tissue loss diseases

Disease dynamics through time At our two initial field sites, Looe Key and Fort Lauderdale, we found differences in lesion appearance (Ft. Lauderdale: subacute often with a bleached band vs. Keys: acute to subacute tissue loss usually lacking a bleached band) and in mortality on tagged M. cavernosa colonies with tissue loss disease. Tagged M. cavernosa colonies at Looe Key had an 84.2% mortality rate after one year but only 15% of the Fort Lauderdale colonies died after a year. There could be regional differences in environmental conditions, which could be affecting M. cavernosa health and immune response, or are affecting presumptive pathogen-host dynamics, allowing the presumed pathogen(s) to have higher virulence at Looe Key. Alternatively, we did not begin monitoring the Ft. Lauderdale colonies until years after the event was first found in that region, and so it could be a natural progression in disease-host dynamics with presumed pathogens or other etiologic factors evolving to a less virulent state. Coral disease outbreaks are often more virulent at the start of an outbreak and then prevalence and severity decrease through time (Brandt et al. 2012, Aeby et al. 2016). During our 2019 tagging study, we found that the mortality rate of M. cavernosa at Looe Key declined from 73.7 % mortality (n=17) between Nov 2018 and May 2019 to 21.4% mortality (n=14) between Nov 2019 and May 2020. There was also a shift in gross lesion morphology with all diseased colonies tagged in 2018 having tissue loss lesions whereas in 2019 the dominant lesion was that of bleached polyps (71.4%) with the remainder being subacute tissue loss. This suggests that the differences in mortality we found among diseased M. cavernosa colonies at Looe Key vs. Fort Lauderdale are explained, in part, by temporal processes. The 2nd site set up in the Keys (Haslun’s reef) appears to be in an earlier stage of SCTLD progression with 100% of the lesions having tissue loss (n=8) and 75% mortality occurring between November 2019 and May 2020. We are continuing to monitor tagged colonies at all three sites (Table 1) to determine whether the pattern of reduced disease severity continues through time.
Review on Quantum-dot Cellular Automata Full adders

Adder is a vital part of any digital circuit. Some adders were developed so far using CMOS technology. However, CMOS technology cannot use beyond some size due to its limitations. There may be an end in the MOS technology in the future. Quantum-dot cellular automata are one of the popular nanotechnologies which deal with the problems of transistor technology (CMOS). Several adders were designed using Quantum-dot Cellular Automata techniques since 2008. In this paper, the adder given by “Md. Abdullah-Al-Shafi and Ali Newaz Bahar”gives better results compared to all existing adders. In this paper, different adders are compared in the Quantum dot cellular architecture method. The various full adders are explained here with their properties such as area, intricacy, latency is studied and also gives the best method for addition is discussed.
RESEARCH ON PERSONALIZED RECOMMENDATION ALGORITHM BASED ON TRUST RELATIONSHIP

Personalized recommendation, as an important technology to overcome information overload, has been widely used in various fields. However, due to the deficiencies and limitations of existing recommendation algorithms, traditional collaborative filtering recommendation algorithm does not consider the impact of multi-source social information, resulting in low recommendation accuracy. To solve the above problems, this paper proposes a personalized recommendation algorithm that integrates user trust relationship. Based on matrix decomposition, this algorithm fully considers the characteristics of explicit trust relationship, implicit trust relationship and trust propagation between users. The algorithm incorporates users' explicit trust relationships and implicit trust relationships through weight factors into the matrix factorization algorithm. The experimental results show that the accuracy of the proposed algorithm is improved significantly.
A Comprehensive Review on Trust Issues, Security and Privacy Issues in Cloud Storage

:- Now a days everywhere talking about cloud computing paradigm and if you look at business enterprises there are lot of initiatives to put everything on a cloud computing . Most of the IT industries are insisting to adopt cloud computing in their business operations. A Cloud Computing is a one infrastructure which can cater to the need of many people and it can do different varieties of services and functionalities such as ,it can store for you, it can compute for you, importantly it can scale for your need, so cloud computing means a provision of infrastructure which is scalable and it can do different varieties of services. A major challenge today in providing cloud computing is data security, access control and privacy of users data, so first we need to identify what are the security issues, security threats and security vulnerabilities present in cloud computing ,after identifying and analyzing all these issues we have to propose a solution model which will provide data security ,access control and data privacy in cloud computing. Information security is one of the significant imperatives for re-appropriated information in a distributed storage condition.
Robust Recovery of Adversarial Examples

Adversarial examples are semantically associated with one class, but modern deep learning architectures fail to see the semantics and associate them to another class. As a result, these examples pose a profound risk to almost every deep learning model. Our proposed architecture can recover such examples effectively with more than 4 × the magnitude of attacks than the capability of the state-of-the-art model, despite having lesser parameters than the VGG-13 model. It is composed of a U-Net with the characteristics of self-attention & cross-attention, which enhances the semantics of the image. Our work also encompasses the differences in the results between Noise and Image reconstruction methodologies.
Lactoferrin binding to Sars-CoV-2 Spike glycoprotein protects host from infection, in�ammation and iron dysregulation.

The anti-SARS-Cov-2 activity of the iron-binding protein Lactoferrrin has been investigated in epithelial and macrophagic cell models using a Pseudovirus decorated with the SARS-CoV-2 Spike glycoprotein. The human and, even more, the nutraceutically available bovine Lactoferrin inhibit pseudoviral infection in all cellular models tested. The bovine protein efficiently counteracts the deleterious effects of purified Spike on iron and inflammatory homeostasis, as shown by restored levels of the main proteins of the iron-handling system and, in the case of macrophagic THP-1 cells, of the proinflammatory cytokines IL-1 β and IL-6. A direct interaction between Lactoferrin and Spike is likely at the basis of the observed effects, as demonstrated by an in vitro pull-down assay. Finally, in silico approaches have been applied to analyze the interactions of human and bovine Lactoferrins with Transferrin Receptor 1, a potential gate for SARS-CoV-2 entry into cells, as well as the binding of the bovine protein to different variants of concern of the SARS-Cov-2 Spike glycoprotein. Our results give hope for the employment of bovine Lactoferrin as an adjuvant of the standard of care therapies in COVID-19 treatment.
4.0. The effects of Kinesio taping-combined exercise on the balance and gait ability of hemiplegic

https://doi.org/10.30574/wjarr.2021. 12.3. 0700 Abstract Purpose : This study aims to investigate the effect of the Kinesio taping method on the balance ability and gait ability of hemiplegic stroke patients by applying it to the lower extremities of the ankle on the affected side, addressing instability and asymmetry by improving the stability of the muscles and ankle joint. Methods : This study confirmed the general characteristics of hemiplegic patients diagnosed with stroke. Kinesio taping was applied to the lower extremities of 15 subjects in the experimental group, and they performed mat and treadmill exercises. In addition, basic mat and treadmill exercises were performed by the 15 subjects in the control group. The exercise regimens were performed 18 times: three times a week for six weeks. Results : In the paired-sample T-test used for within-group comparison of BBS, TUG and stance time on nonparetic side value, there was a significant difference between pre- and post-test for experimental group. However, in the control group, there was a statistically significant difference only in TUG. In the inter-group comparison, the experimental group showed a statistically significant improvement in BBS, TUG and stance time on nonparetic side value compared to the control group. Conclusion: The experiment’s results demonstrate that the application of Kinesio taping-combined exercise provides stability of the muscles and ankle joints of the lower extremities during walking by improving balance ability, and improves overall gait stability by increasing the stance phase time of the affected side in hemiplegic stroke
KafkaDirect: Zero-copy Data Access for Apache Kafka over RDMA Networks

Apache Kafka is an open-source distributed publish-subscribe system, which is widely used in data centers for messaging between applications, log aggregation, and stream processing. The existing Kafka implementation uses TCP/IP for communication, which has various inefficiencies such as a high message dispatch cost due to OS involvement and excessive memory copies. Recently, the availability of cost-effective RDMA-capable network controllers within data centers and cloud infrastructures have encouraged many modern applications to adopt RDMA networking, which offers the potential to outperform classical TCP/IP. We introduce KafkaDirect, an extension to Apache Kafka, that uses RDMA to accelerate the three most network intensive datapaths: record production, record replication, and record consumption. In this work, we explore the design choices including which RDMA operations to use to take full advantage of offloaded communication. Our RDMA design relies on one-sided RDMA requests to attain true zero-copy communication completely avoiding the need for using intermediate buffers in Kafka servers, thereby ensuring low latency and high throughput communication. KafkaDirect can offer up to 9x increase in throughput for both Kafka producers and Kafka consumers, and can provide 4x and 50x reduction in latency for Kafka producers and Kafka consumers, respectively.
Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in . 1300 PTSD cases and . 2000 trauma-exposed control subjects (ages 6.2 – 85.2 years) by the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta Analysis – Psychiatric Genomics Consortium) PTSD working group. Cortical regions in the network were rank ordered by the effect size of PTSD-related cortical differences in CT and SA. The top- n ( n = 2 – 148) regions with the largest effect size for PTSD . non-PTSD formed hypertrophic networks, the largest effect size for PTSD , non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n -region network was the average of all positive pairwise correlations and was compared with the mean SC of 5000 randomly generated n -region networks. RESULTS: Patients with PTSD, relative to non-PTSD control subjects, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex, and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The SC networks that are perturbed in PTSD comport with converging evidence from resting-state functional connectivity networks and networks affected by in ﬂ ammatory
Validation of Copernicus Sentinel-3/OLCI Level 2 Land Integrated Water Vapour product

Abstract. Validation of the Integrated Water Vapour (IWV) from Sentinel-3 Ocean and Land Colour Instrument (OLCI) was performed as a part of the “ESA/Copernicus Space Component Validation for Land Surface Temperature, Aerosol Optical Depth and Water Vapour Sentinel-3 Products” (LAW) project. High-spatial-resolution IWV observations in the near-infrared spectral region from the OLCI instruments aboard the Sentinel-3A and Sentinel-3B satellites provide continuity with observations from MERIS (Medium Resolution Imaging Spectrometer). The IWV was compared with reference observations from two networks: GNSS (Global Navigation Satellite System) precipitable water vapour from the SuomiNet network and integrated lower tropospheric columns from radio-soundings from the IGRA (Integrated Radiosonde Archive) database. Results for cloud-free matchups over land show a wet bias of 7 %–10 % for OLCI, with a high correlation against the reference observations (0.98 against SuomiNet and 0.90 against IGRA). Both OLCI-A and OLCI-B instruments show almost identical results, apart from an anomaly observed in camera 3 of the OLCI-B instrument, where observed biases are lower than in other cameras in either instrument. The wavelength drift in sensors was investigated, and biases in different cameras were found to be independent of wavelength. Effect of cloud proximity was found to have almost no effect on observed biases, indicating that cloud flagging in the OLCI IWV product is sufficiently reliable. We performed validation of random uncertainty estimates and found them to be consistent with the statistical a posteriori estimates, but somewhat higher.
2021 update on $\varepsilon_K$ with lattice QCD inputs

We present recent updates for $\varepsilon_K$ determined directly from the standard model (SM) with lattice QCD inputs such as $\hat{B}_K$, $|V_{cb}|$, $|V_{us}|$, $\xi_0$, $\xi_2$, $\xi_\text{LD}$, $f_K$, and $m_c$. We find that the standard model with exclusive $|V_{cb}|$ and other lattice QCD inputs describes only 66\% of the experimental value of $|\varepsilon_K|$ and does not explain its remaining 34\%, which leads to a strong tension in $|\varepsilon_K|$ at the $4.5\sigma \sim 3.7\sigma$ level between the SM theory and experiment. We also find that this tension disappears when we use the inclusive value of $|V_{cb}|$ obtained using the heavy quark expansion based on the QCD sum rule approach.
EFFECTS OF DIFFERENT FERTILIZERS FORMULAS ON THE GROWTH AND DEVELOPMENT OF LEAF MUSTARD,

Fertilizers are the most important and complex nutrients for crop plants in particular for grain yield and quality. The composition of the fertilizer as well as the essential elements that influence the growth of the crop need to be clearly identified. Due to that, this study was carried out to investigate the effect of different fertilizer formulation on the leaf mustard ( Brassica juncea ) growth. High nitrogen, phosphorus and potassium fertilizers were used to investigate their effects on the morphometric size of the leaves, plant height and the leaf area index of the leaf mustard. Results showed that the application of different formulation of fertilizer improves the growth of leaf mustard compared to control. Leaf mustard with the high phosphorus treatment recorded an increase in plant height and the leaf area index (LAI). Lamina length (LL) range is shown between phosphorus and control (1.11 cm), while the range of lamina width (LW), left width (WL) and right width (WR) are between potassium and control about 0.57 cm, 0.28 cm and 0.28 cm, respectively. Overall, there is a significant difference between the leaf mustard leaves in different high element fertilizers compared with all of the variable, F (15,1024) = 29.26, p <0.05. However, the highest mean (in cm) in height was obtained in high phosphorus (16.64) followed by high potassium (16.50), control (16.41) and high nitrogen (16.35) fertilizers with a mean range of 0.29 cm (F (3,176) = 0.47; p >0.05, no significant difference). The highest mean in LAI was obtained when treated with a high phosphorus fertilizer (0.47 m 2 ). The mean difference of LAI of high phosphorus compared to high potassium, high nitrogen and control is 0.02 m 2 , 0.08 m 2 and 0.12 m 2 . There is no significant differences between the LAI in different high element fertilizers with F (3,176) = 0.15; p >0.05. Further study should be conducted to determine the effects of different fertilizers on the growth of other vegetables and fruit quality.
The Use of Gamification Tools to Boost Students’ Engagement and Motivation

Gamification tools are integrated into learning management systems (LMS) as an instrument to help increase students' motivation and engagement in class. These tools include badges, rewards, achievements, leaderboards, and others. This study used the mixed methods of research to determine the appropriate gamification tools for selected Industrial Engineering (IE) courses in a higher education institution (HEI) and determined students' acceptance of the use of these tools. Literatures indicated that Achievements and Rewards tools could be utilized in all of the courses. Similarly, the Technology Acceptance Model (TAM) results showed that gamification tools generated positive responses because of their usefulness and ease of use. However, students lack the intent to use the gamification tools. For the students to use the gamification tools, they need to exhibit a confident attitude in using them. This study's findings could be used as a basis for designing and evaluating the gamification tools available in various LMS.
Comparison of Bayesian Regularized Neural Network, Random Forest Regression, Support Vector Regression and Multivariate Adaptive Regression Splines Algorithms to Predict Body Weight from Biometrical Measurements in Th alli Sheep Regresyon Uzanımları Algoritmalarının Karşılaştırılması

: In this study, it is aimed to compare several data mining and artificial neural network algorithms to predict body weight from biometric measurements for the Th alli sheep breed. For this purpose, the prediction capabilities of Bayesian Regularized Neural Network (BRNN), Support Vector Regression (SVR), Random Forest Regression (RFR) and Multivariate Adaptive Regression Splines (MARS) algorithms were comparatively investigated. To measure the predictive performances of the evaluated algorithms, body measurements such as body length, heart girth, ear length, ear width, head width, head length, withers height, rump length, rump width neck length, neck width of Th alli sheep were used for predicting the body weight. In this context, 270 female Th alli sheep were used to predict body weight. Model comparison criteria such as root-mean square error (RMSE), standard deviation ratio (SDR), performance index (PI), global relative approximation error (RAE), mean absolute percentage error (MAPE), Pearson’s correlation coefficient (r), determination of coefficient (R 2 ) and Akaike’s information criteria (AIC) were used to compare all algorithms. In conclusion, the MARS algorithm can be recommended to enable breeders to obtain an elite population of Th alli sheep breed.
Competition between renewable and traditional power producers: how spot market design influences the emergence of strategic investments in renewable capacity Working Paper

We introduce a theoretical framework for the analysis of competition between a traditional and a renewable generator in a spot electricity market where the electricity from renewable sources is always the ﬁrst to be dispatched. The model accounts for randomness in the availability of renewable capacity due to the partial unpredictability of weather conditions. Competition is studied through a model of strategic investments for entry deterrence with two post entry competition settings: the Cournot framework in a two stage game and the dominant ﬁrm-competitive fringe setting in a three stage game. Both models show that the renewable producer exploits merit order rule by strategically investing in a large generation capacity in order to crowd out the production of its rival. The analysis has important implications for electricity market design.