Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the Bonding Strength between Various Dental Zirconia Models and Human Teeth for Dental Posts through In Vitro Aging Tests

In dentistry, root canal treatment reduces support of the tooth, making it necessary to insert a cylindrical body into the treated tooth to strengthen the crown. In the past, metal or fiberglass was often used. However, metal is too different in color from teeth, so the esthetics are poor, and fiberglass is not as strong as metal. Therefore, an alternative is zirconia, which has the characteristics of high light transmittance, esthetics, good biocompatibility, and high breaking strength. The surface morphology and composition of zirconia ceramics are the key to their bond strength with teeth. Therefore, in this study, the surface characteristics of different brands of zirconia commonly used in clinical practice were evaluated in terms of their surface morphology and surface elements. The surface was modified by sandblasting, and its effect on the bonding strength was discussed. Finally, the stability of the material was evaluated through artificial aging. The results showed that the surface roughness of the zirconia specimens increased after sandblasting, whereas the surface microhardness decreased. The shear test results showed that the 3D shape of the zirconia surface could help improve the bonding strength. The bonding strength of DeguDent increased the most after sandblasting. After 20,000 cycles of aging treatment, the shear strength of each specimen decreased. Field emission scanning electron microscopy results showed that the adhesive remained intact on the surface of zirconia, indicating that adhesion failure occurred between the adhesive and the teeth. This confirms that sandblasting can improve the bonding strength of zirconia. Based on the results obtained, it was concluded that the surface roughness of zirconia is the main factor affecting the bond strength.
Influence of selected company characteristics on voluntary disclosure of intangible assets in listed companies

Abstract Generally, companies have been faced with problem that ranges from non-disclosure to partial disclosure of intangible assets (IAs). This however, distorts the oversight function of the directors of companies from producing full and accurate financial information in the annual reports for various segments of the society for investment decisions. Intangible assets are generally pivotal due to its value creating attributes and enhancing healthy competitive advantages. This study examined the influence of certain company specific characteristics on voluntary disclosure of IAs of listed companies in Nigeria for 2011–2018 as a case study. Statistical analysis was conducted and the result showed a significant positive relationship between performances and industry size as factors that drives voluntary disclosure of intangible assets (IAs) in Nigeria while leverage and listing age does not have any impact on voluntary disclosure of IAs. Consequently, we recommend that government should provide incentives to companies that engage in voluntary disclosure of their IAs. Again, whistle blowing strategies may be adopted by shareholders and concerned public at least to extol compliance, continuous training and a shift in mindset of managers is also recommended.
Detecting COVID 19 using Deep Learning

Abstract: Corona virus disease 2019 (COVID 19) is defined as illness caused by novel corona virus now called severe acute respiratory syndrome corona virus 2 (SARS-Cov-2; formally called as 2019-nCov), which was first identified in Wuhan City, Hubei Province, China. The spreading of COVID 19 is very fast throughout the world. World economy as well as public health has been facing a devastating effect caused by COVID 19. Hence detecting COVID 19 is challenging task even we have multiple methods like RT-PCR, COVID kits. The RT-PCR may not available in all laboratory, even exists which take some time to process and get reports and COVID 19 test kits may not available in all places. So, the main intention of this paper is to detect COVID 19 with in low budget, less time and accurate results. We have trained deep transferred learning models like ResNet-50, ResNet-101 using COVID positive, Normal, Viral Pneumonia chest x-rays. ResNet-50, ResNet-101 is pre-trained deep learning neural network. ResNet-50 provides 98% of accuracy where ResNet-101 gives us 97% of accuracy. Keywords: COVID 19, Deep Learning, ResNet-50, Transferred Learning, Artificial Intelligence.
Controlling fungal growth in sesame (Sesamum indicum L.) seeds with γ-irradiation: impacts on some properties of sesame oil

This study investigated the free fatty acids, fatty acid profile, total phenolics, and antioxidant activity of sesame seed oil extracted from γ-irradiated seeds and the decontamination effects of the treatment on fungal incidence in the seeds. Gamma irradiation reduced (P ≤ 0.05) fungal growth and colony forming units of sesame seeds in a dose-dependent manner. The free fatty acid content of sesame oil decreased (P ≤ 0.05) in irradiated samples compared to non-radiated controls, but there was no difference (P ≥ 0.05) between samples treated at doses ≥ 1.0 kGy. A concomitant (P ≤ 0.05) increase in total phenolic and scavenging activity was observed in the oil extracted from γ-irradiated sesame seeds in comparison with non-radiated samples, while free fatty acid (FFA) content decreased. The results obtained in the present study demonstrate that γ-irradiation at low doses can be used as an effective post-harvest preservation method for sesame seeds without a major effect on the quality of sesame oil.
Training An Embedded Object Detector For Industrial Settings Without Real Images

In an industrial environment, object detection is a challenging task due to the absence of real images and real-time requirements for the object detector, usually embedded in a mobile device. Using 3D models, it is however possible to create a synthetic dataset to train a neural network, although the performance on real images is limited by the domain gap. In this paper, we study the performance of a Convolutional Neural Network (CNN) designed to detect objects in real-time: Single-Shot Detector (SSD) with a MobileNet backbone. We train SSD with synthetic images only, and apply extensive data augmentation to reduce the domain gap between synthetic and real images. On the T-LESS dataset, SSD performs better than Mask R-CNN trained on the same synthetic images, with MobileNet-V2 and MobileNet-V3 Large as backbone. Our results also show the huge improvement enabled by an adequate augmentation strategy.
The Moderation Role of Transformational Leadership in the Effect of Instructional Leadership on Teacher Professional Learning and Instructional Practice: An Integrated Leadership Perspective

Aim: While integrated leadership has received significant scholarly attention in the past decade, most existing research in this vein has focused on its impact on student achievement and often dismissed how it might be related to instructional practices, which are at the center of many school reforms. In this research, we examined the relationship between integrated leadership and teacher professional learning and teacher practices in Turkey, where educational policy makers have recently introduced several school reform initiatives. More specifically, we aimed to examine the moderating function of transformational leadership in the relationship between instructional leadership and teacher practices, with an emphasis on the mediating role of teacher professional learning. Research Design: We conducted this study with a cross-sectional design and moderated mediation model. Data collected from 616 teachers working in a mix of primary and secondary schools in Turkey were analyzed using structural equation modeling and bootstrapping tests. Findings: We found evidence that transformational leadership acted as a moderator of the indirect effect of principal instructional leadership on teachers' instructional practices through teacher professional learning. Implications: This study adds to the accumulated body of knowledge on the effects of school leadership by concluding that the effect of instructional leadership on teacher learning and practice is contingent upon the extent to which principals enact transformational leadership. School principals who adopt a more comprehensive leadership approach that combines instructional leadership and transformational leadership practices can maximize their effects on student achievement through teacher learning, and better address the ever-growing demands of educational reforms.
Comparable Discrimination of Soil Constituents Using Spectral Reflectance Data (400–1000 nm) Acquired with Hyperspectral Radiometry

Currently, a gap exists in inventorying and monitoring the impact of land use and management on soil resources. Reducing the number of samples required to determine the impact of land management on soil carbon (C) and mineral constituents via proximal sensing techniques such as hyper-spectral radiometry can reduce the cost and personnel required to monitor changes in our natural resource base. Previously, we used an expensive, high signal-to-noise ratio (SNR) field spectrometer to correlate soil constituents to hyperspectral diffuse reflectance (HDR), over the 350–2500 nm (VIS-SWIR) wavelength range. This research is an extension of preceding research but focuses solely on the 400–1000 nm (VIS-NIR) region of the electromagnetic spectrum. This region can be measured using less expensive (albeit with lower SNR), miniaturized, field spectrometers that allow minimal sample preparation. Our objectives are to: (1) further evaluate the use of soil HDR in the visible and near-infrared (VIS-NIR) region acquired using an expensive field hyperspectral spectroradiometer for prediction of soil C and selected fractions and nitrogen (N) constituents, (2) repeat the above measurements using HDR data from samples examined in objective (1) using lower SNR hyperspectral radiometers, and (3) add to the limited literature that addresses determinations of selected soil properties using proximal sensing in the VIS-NIR region. Data analyzed in this study confirms that good to satisfactory prediction equations for soil constituents can be developed from spectral reflectance data within the 400–1000 nm wavelength region obtained using relatively inexpensive field radiometers. This application could reduce the time and resources required to monitor gains or losses in carbon constituents, information that can be used in programing such as Conservation Technical Assistance (CTA), the Conservation Reserve Program (CRP) and Climate-smart agriculture (CSA).
A study of emerging trends in digital preservation literature: An analysis of journal articles presented in course syllabi

The field of digital preservation education is evolving due to the rapid developments in the digital preservation field, and as educators and researchers respond to these developments. One way to understand trends in education is through the examination of course syllabi and through the assigned course readings, as instructors often utilize and integrate core and seminal literature in these courses. This study aims to understand the emerging topics and trends in digital preservation education through the examination of these course readings. This study examines these topics and trends through an analysis of the literature assigned digital preservation courses at North American ALA (American Library Association)-accredited Master’s in Library and Information Science programs through bibliometric analysis, topic modeling, and visual analysis of the citation data.
Chemical fractions and response of cauliflower (Brassica oleracea var. botrytis) to soil applied boron

Abstract The present study was conducted with an objective to estimate the distribution of boron (B) application in various soil fractions and their plant response for assessing the availability in the soil. Two soils (alluvial and red soil) and five levels of B (0, 0.5, 1, 2 and 3 mg B kg−1 soil) were applied in the pot experiment, and pots were sown with cauliflower (Sabour Agrim) arranged in a completely randomized block design (CRD) with three replications. Result showed that the curd yield of cauliflower increased significantly upto 2 mg B kg−1 soil irrespective of soils. The percent yield increase was 14.78 and 15.01 in alluvial and red soil over the control, respectively. The initial total B content was 35.88 (alluvial soil) and 15.51 (red soil) mg·kg−1. The mean content of Fraction I, II, III, IV and V in alluvial soil was 1.11, 1.54, 0.65, 1.49, and 95.18% and in red soil was 2.68, 4.47, 6.62, 2.50, and 83.59% of the total soil B, respectively. For changes in amount of B fractions due to B applications there was significant effect on all the fractions except Fraction II. The increase in apparent B uptake was 0.43 mg B kg−1 in alluvial soil and 0.25 mg B kg−1 in red soil over the control (0 mg B kg−1 soil). Regression equation of yield and B fractions showed the relationship between first four fractions to the yield. Residual fraction was found to be collinear during calculation. Overall the study predicted the bioavailability and dynamics of B in the two distinct soils.
Transformational Leadership and Turnover Intentions: The Mediating Role of Employee Performance during the COVID-19 Pandemic

The purpose of this research paper is to explain the impact of transformational leadership on employees’ turnover intentions in light of the mediating role of their individual performance. More precisely, we attempt to explain (a) how TL connects to employees’ turnover intentions, (b) how TL connects to employee performance, (c) how employee performance connects to turnover intentions, and (d) how employee performance mediates the connection between intentions and TL. Four hundred and seventy-eight Turkish healthcare professionals participated in this research. The results revealed that employee performance mediates the connection between turnover intentions and TL. In other words, transformational leaders encourage employee performance, which in turn decreases their turnover intentions. This research has important implications for reducing turnover in workplaces and increasing employee performance by facilitating a setting for high performance, since employees who are supported by their leaders are generally more committed to their organizations and exhibit better performance. This research answers calls to study the mediating function of the TL procedure, since the mediation clarifies the circumstances under which TL relates to the favorable results.
Factors affecting the delivery of optimum health and safety on construction projects during the covid-19 pandemic in Zimbabwe

Purpose The purpose of this study was to investigate the perceptions of construction professionals relative to factors that affect the delivery of optimum health and safety (H&S) on construction projects during the COVID-19 pandemic. Design/methodology/approach The study adopted a quantitative design which entailed the distribution of a web-based questionnaire among construction professionals, namely, architects, construction/project managers, engineers, H&S managers and quantity surveyors working for contractors and construction consultants in Zimbabwe. The data were analysed with descriptive and inferential statistics. Factor analysis was used to reveal interrelated significant sets of factors affecting the delivery of optimum H&S. Findings Factor analysis revealed nine components/factors: change and innovation-related, monitoring and enforcement-related, production-related, access to information and health service-related, on-site facilities and welfare-related, risk assessment and mitigation-related, job security and funding-related, cost-related and COVID-19 risk perception-related factors as the significant factors affecting the delivery of optimum H&S during the COVID-19 pandemic in Zimbabwe. Research limitations/implications The results highlighted the need for social dialogue among construction stakeholders to support initiatives that will enhance the delivery of H&S on construction projects. Construction stakeholders may find the results useful in highlighting the areas that need improvement to protect workers’ H&S during the pandemic. However, the small sample limits the generalisability of the results to construction sectors in other regions. Originality/value The study investigated factors affecting the delivery of optimum H&S during the COVID-19 to inform interventions to enhance H&S.
KINET: A Non-Invasive Method For Predicting Ki67 Index Of Glioma

In this paper, a multimodal magnetic resonance imaging (MRI) and heterogeneous metadata (including age, gender) dataset containing263 patients was established. Based on this dataset, a new multimodal deep neural network (KiNet) was proposed, aiming to effectively predict the Ki67 index in gliomas in a non-invasive way by fusing multimodal MRI features and metadata. We adopted a five-fold crossvalidation approach to verify the performance of the network. KiNet achieved results with an AUC of 0.79 and a kappa coefficient of 0.47. The proposed approach’s outperformance indicated the feasibility of predicting the Ki67 index in gliomas in a non-invasive way.
An investigation of deposit formation in the injector, spray characteristics, and performance of a diesel engine fueled with preheated vegetable oil and diesel fuel

ABSTRACT Diesel engines have considered as the popular internal combustion engine used in transport, agriculture-forestry-fishery, and power generator due to their utility. Stringent norms of the environment, increasing demand of energy, and depleted fossil fuel have led to looking for an alternative and renewable energy source. Jatropha oil-based fuels were considered as the appropriate energy source in the future to replace traditional diesel fuel. However, some problems such as coke and deposit formation resulting in the changes in fuel spray characteristics and engine performance were encountered because of high kinematic viscosity and density of straight Jatropha oil when used for diesel engines. The aim of this study is to improve the drawbacks of Jatropha oil by heating before being fueled a 4-stroke–4-cylinder diesel engine. The deposit formation around the injector tips, fuel spray characteristics (including penetration length and cone angle), combustion, and emissions of the above-mentioned diesel engine running on preheated Jatropha oil and diesel fuel after 300 h were practically measured and compared to the first hour (0 h). After completion of the long-term 300 h test, some results of the test engine fueled with preheated Jatropha oil such as a huge amount of deposits around the injectors tips resulting in partly clogging the nozzle holes and increasing penetration length to 10.52%, a decrease of 30.73% for engine power and 7.28% for NOx emissions, an increase in 8.63% of HC and 6.96% of CO emissions were detected; meanwhile, the above parameters for diesel fuel after 300 h of test were insignificantly different from the first hour. Moreover, the element components in deposits were also thoroughly analyzed by EDS, and optical observation was assisted by SEM.
Assessment of the organic fruit quality of local and introduced apricot cultivars grown in Tunisia: morphological and physico-chemical attributes

Purpose Organic farming system was considered to increase fruit quality and improve food safety. Moreover, many consumers prefer organic products due to their better taste. Among the Prunus species, apricot is well grown in the world due to its good taste and multi uses. Tunisian apricot cultivars are cultivated in many areas, well adapted and characterized by varied pomological characteristics. In fact, due to the demand of the fresh market, there is growing introduction of commercial cultivars with firmness, visual appeal and flavor as principal traits demanded. Here, we aimed to assess the quality of organic apricot among local and introduced cultivars, in order to identify the cultivar(s) with better fruit quality performances. Research method: For this purpose, morphological (attractiveness, shape, surface, ground color of skin, etc.) and physico-chemical (weight, width, TSS, titrable acidity, etc.) attributes of fruit were studied according to the international descriptors of apricot and to the other investigations on fruit quality. Findings: Few differences of morphological attributes but high significant differences for most physico-chemical attributes of organic fruit were observed between local and introduced apricot cultivars. The organic cultivation system promotes the production of high fruit quality (high value of TSS: > 13°Brix, low value of acidity and big size) for the 3 local cultivars. Limitations: No limitations were founded. Originality/Value: Compared to the introduced apricot cultivars, the local ones are characterized by the performances of their fruit quality and also by their best adaptation under organic cultivation system.
Sensory processing in autism across exteroceptive and interoceptive
 domains.

Autism is a neurodevelopmental condition currently defined and diagnosed by the presence of lifelong difficulties in the domains of social interaction, social communication and social imagination, as well as restricted or stereotyped interests or behaviors. While early conceptualizations of autism incorporated sensory differences, they have been relatively neglected in proceeding years. Recognition of sensory differences in formal diagnostic criteria is now starting to be reintroduced. To date, sensory research suggests that both external and internal signal perception diverges in autistic individuals, but has been largely segregated between interoceptive and exteroceptive domains, impeding the construction of a general framework for understanding how perceptual experiences may differ for autistic individuals. We review recent findings detailing exteroceptive and interoceptive processing in autism and discuss their neurobiological basis. Based on this synthesis, we suggest that future work should be sensitive to individual differences to determine if divergent patterns of sensory processing are observed within individuals, rather than looking for global changes at a group level. Determining whether divergent patterns of exteroceptive and interoceptive sensory processing may contribute to prototypical social and cognitive characteristics of autism may drive new directions in our conceptualization of autism.
Maternal socio-ecological antecedents and children’s school readiness

ABSTRACT Objective This study aimed to examine the effects of maternal demographics, interaction quality, and children’s gender on school readiness using different informants. Method Participants recruited for this study included 25 kindergarten teachers, 271 mothers, and their kindergarten children (169 girls and 102 boys with a mean age of 4.28 years). Mothers were asked to provide demographic information and complete a questionnaire regarding their interaction quality with their children. Kindergarten teachers assessed children’s school readiness. Results Structural equation modelling was employed to examine two layers: the effect of maternal antecedents on maternal interaction quality, and the role of maternal antecedents, maternal interaction quality, and child’s gender on school readiness. The results indicated that maternal interaction quality was affected by maternal age, family size, and working hours. Maternal interaction quality, maternal education, and child’s gender were triadic modalities found to be associated with school readiness. Conclusions This study provides additional insights into the socioecological factors that shape children’s academic outcomes and emphasizes school readiness as a salient factor affirming school entrance affected by multiple factors that may not solely depend on the child. This may ensure the requirement for comprehensive and inclusive directions concerning school readiness.