Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancement of Oral & Maxillofacial Surgery with CBCT: A Review Study

: Cone Beam Computed Tomography (CBCT) is a most valuable imaging technique used in oral and maxillofacial surgery. The introduction of CBCT for imaging the oral and maxillofacial region, holds a major change from two dimensional to three-dimensional approach. CBCT provides a complete 3D view of the oral and maxillofacial structures with high resolution which helps for accurate diagnosis, treatment planning and postoperative outcomes compared to conventional 2D images. Radiation exposure to the patient is very low in CBCT. The main clinical applications of CBCT arein oral and maxillofacial surgery, orthodontics, periodontics and in endodontics. The aim of this article is to review on the advantages, disadvantages and clinical applications of CBCT in the oral& maxillofacial surgery.
Does the Shield Effect of CSR Work in Crises? Evidence in Korea

This paper investigates the impact of corporate social responsibility (CSR) on shareholders’ wealth during market downturn, focusing on the market crash caused by the COVID-19 pandemic and its aftermaths. We evaluate the relationship between firms’ CSR and stock returns using a sample of 803 firms listed on the Korean stock market. The results of our study reveal that firms’ pre-crisis CSR activities do not protect shareholders’ wealth during the crisis; in fact, they negatively affected stock returns during the COVID-19 crisis. This finding is consistent across several robustness tests and challenges the prevailing notion that CSR is solely a philanthropic endeavor. This study suggests that firms need to reconsider their CSR approach in order to better align it with shareholders’ interest.
Social capital of managers and firm operational performance of SMEs in Ho Chi Minh City

Operational efficiency, a dominant concern for businesses, is always an issue for the researchers to find the solutions for helping the businesses enhance their corporate performance. In previous studies on the performance of enterprises in general and small and medium-sized enterprises in particular, most of the studies focused on financial factors, but did not pay much attention to nonfinancial factors. Operational efficiency depends on financial and non-financial factors, of which, non-financial factors are diverse (human capital, social capital, psychological capital, non-financial risk, management efficiency, etc.). theory, applied science, etc). Social capital is a new area of research in Vietnam, which is closely related to the economic field. In Vietnam, interdisciplinary theoretical applied research is a new research trend (economic theory combined with social theory), which this study follows. The study applies the social capital theory ``The strength of weak ties'' of Granovetter, theory ``The network Structure: of Burt and ``A Network theory of social capital'' of Lin to examine the influence of social capital on firm performance. The research model comprises 5 factions of independent variables representing social capital (Relational (REH), Cognitive (CSC), Social Interaction Ties (SIT), Trust (TRU) and Structural (SSC) and a group of dependent variables (Firn Operational Performance). Structural Equation Modeling was used to analyze data collected from a survey of 378 SME managers in Ho Chi Minh City based on a convenient method. The analysis results demonstrate that all aspects of social capital have positive effects on business operational performance, of which Structural Social Capital (SSC) and trust (TRU) are the two most influential groups. The research results help business administrators better understand social capital's importance, thereby improving business performance through maintaining and developing managers' social capital.
Neonatal Care Unit Interventions on Preterm Development

Prematurity is becoming a real public health issue as more and more children are being born prematurely, alongside a higher prevalence of neurodevelopmental disorders. Early intervention programs in Neonatal Intensive Care Units (NICUs) correspond to these uni- or multi-sensorial solicitations aiming to prevent and detect complications in order to support the development of preterm infants. This article aims to distinguish sensory intervention programs according to the gradient of the type of solicitations, uni- or multi-modal, and according to the function of the person who performs these interventions. Uni-sensorial interventions are essentially based on proprioceptive, gustatory, or odorant solicitations. They allow, in particular, a reduction of apneas that support the vegetative states of the preterm infant. On the other hand, the benefits of multi-sensory interventions seem to have a longer-term impact. Most of them allow the support of the transition from passive to active feeding, an increase in weight, and the improvement of sleep-wake cycles. These solicitations are often practiced by caregivers, but the intervention of parents appears optimal since they are the main co-regulators of their preterm child’s needs. Thus, it is necessary to co-construct and train the parents in this neonatal care.
Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling

The input variable selection problem has recently garnered much interest in the time series modeling community, especially within water resources applications, demonstrating that information theoretic (nonlinear)‐based input variable selection algorithms such as partial mutual information (PMI) selection (PMIS) provide an improved representation of the modeled process when compared to linear alternatives such as partial correlation input selection (PCIS). PMIS is a popular algorithm for water resources modeling problems considering nonlinear input variable selection; however, this method requires the specification of two nonlinear regression models, each with parametric settings that greatly influence the selected input variables. Other attempts to develop input variable selection methods using conditional mutual information (CMI) (an analog to PMI) have been formulated under different parametric pretenses such as k nearest‐neighbor (KNN) statistics or kernel density estimates (KDE). In this paper, we introduce a new input variable selection method based on CMI that uses a nonparametric multivariate continuous probability estimator based on Edgeworth approximations (EA). We improve the EA method by considering the uncertainty in the input variable selection procedure by introducing a bootstrap resampling procedure that uses rank statistics to order the selected input sets; we name our proposed method bootstrap rank‐ordered CMI (broCMI). We demonstrate the superior performance of broCMI when compared to CMI‐based alternatives (EA, KDE, and KNN), PMIS, and PCIS input variable selection algorithms on a set of seven synthetic test problems and a real‐world urban water demand (UWD) forecasting experiment in Ottawa, Canada.
Monotone Regression Splines in Action

A-bstract. Piecewise polynomials or splines extend the advantages of polynomials to include greater flexibility, local effects of parameter changes and the possibility of imposing useful constraints on estimated functions. Among these constraints is monotonicity, which can be an important property in many curve estimation problems. This paper shows the virtues of monotone splines through a number of statistical applications, including response variable transformation in nonlinear regression, transformation of variables in multiple regression, principal components and canonical correlation, and the use of monotone splines to model a dose-response function and to perform item analysis. Computational and inferential issues are discussed and illustrated.
Health Disparities and Comparison of Psychiatric Medication Use before and after the COVID-19 Pandemic Lockdown among General Practitioner Practices in the North East of England

Background: Psychiatric medications play a vital role in the management of mental health disorders. However, the COVID-19 pandemic and subsequent lockdown limited access to primary care services, leading to an increase in remote assessment and treatment options to maintain social distancing. This study aimed to investigate the impact of the COVID-19 pandemic lockdown on the use of psychiatric medication in primary care settings. Methods: We conducted a retrospective claims-based analysis of anonymized monthly aggregate practice-level data on anxiolytics and hypnotics use from 322 general practitioner (GP) practices in the North East of England, where health disparities are known to be higher. Participants were all residents who took anxiolytics and hypnotics from primary care facilities for two financial years, from 2019/20 to 2020/21. The primary outcome was the volume of Anxiolytics and Hypnotics used as the standardized, average daily quantities (ADQs) per 1000 patients. Based on the OpenPrescribing database, a random-effect model was applied to quantify the change in the level and trend of anxiolytics and hypnotics use after the UK national lockdown in March 2020. Practice characteristics extracted from the Fingertips data were assessed for their association with a reduction in medication use following the lockdown. Results: This study in the North East of England found that GP practices in higher health disparate regions had a lower workload than those in less health disparate areas, potentially due to disparities in healthcare utilization and socioeconomic status. Patients in the region reported higher levels of satisfaction with healthcare services compared to the England average, but there were differences between patients living in higher versus less health disparate areas. The study highlights the need for targeted interventions to address health disparities, particularly in higher health disparate areas. The study also found that psychiatric medication use was significantly more common in residents living in higher health disparate areas. Daily anxiolytics and hypnotics use decreased by 14 items per 1000 patients between the financial years 2019/20 and 2020/21. A further nine items per 1000 decreased for higher health disparate areas during the UK national lockdown. Conclusions: People during the COVID-19 lockdown were associated with an increased risk of unmet psychiatric medication demand, especially for higher health disparate areas that had low-socioeconomic status.
Object-Oriented Clustering Approach to Detect Evolutions of ENSO-Related Precipitation Anomalies over Tropical Pacific Using Remote Sensing Products

Precipitation extremes driven by the El Niño–Southern Oscillation (ENSO) are one of the critical ways in which the ENSO impacts the global climate, specifically in the tropical Pacific, where they have the potential to cause extreme weather conditions. However, existing approaches struggle to effectively identify the evolution of ENSO-related precipitation anomalies that change rapidly in spatial distribution. To address this challenge, we propose the object-oriented spatiotemporal clustering approach using remote sensing products (OSCAR) for detecting evolutions of ENSO-related precipitation anomalies. The OSCAR was validated using simulated datasets and applied to precipitation anomalies over the tropical Pacific. The simulation experiment demonstrates that the OSCAR outperforms the dual-constraint spatiotemporal clustering approach (DcSTCA) in accuracy, particularly for rapidly evolving precipitation anomaly variations. The application of the OSCAR demonstrates its ability to recognize the evolution of ENSO-related precipitation anomalies over the tropical Pacific, which may offer valuable references for global climate change research.
Analog RF Circuit Sizing by a Cascade of Shallow Neural Networks

A deep neural network architecture for the automatic sizing of analog circuit components is proposed, with a focus on radio frequency (RF) applications in the 2 to 5-GHz region. It addresses the challenges of the typically small number of examples for network training and the existence of multiple solutions, of which impractical values for integrated circuit implementation. We address these issues by restricting the learning to one component size at a time, thanks to a cascade of dedicated shallow neural networks (SNNs), where each network constrains the prediction of the next ones. Moreover, the SNNs are individually tuned by a genetic algorithm for the prediction order and accuracy. This reduction of the solution space at each step allows the use of small training sets, and the introduced constraints between SNNs handle component interdependencies. The method is successfully validated on three different types of RF microcircuits: 1) a low-noise amplifier (LNA); 2) a voltage-controlled oscillator (VCO); and 3) a mixer, using 180 and 130-nm CMOS implementations. All the predictions were within 5% of the true values, both at the component and performance levels, and all the responses were obtained in less than 5 s, after 4 to 47 min. Training on a regular PC station. The obtained results show that the proposed method is fast and applicable to arbitrary analog circuit topologies, with no need to retrain the developed neural network for each new set of desired circuit performances.
Influence of various phosphoric concentrations on tissue and intracellular metabolism of Cyprinus Carpio L. in aquatic habitat

The research relevance is predefined by the fact that under the influence of various factors of the aquatic environment, changes in the speed and direction of the metabolic processes of hydrobionts are recorded. The research aims to study the influence of different inorganic phosphorus content in the aquatic environment on the indicators of phosphorus-calcium exchange in fish. The methods of thin-layer chromatography and variational statistics were used. Glandular tissues of the gills, liver and kidneys of fish were analyzed. To study the influence of inorganic phosphorus in the water environment on some indicators of intracellular metabolism, mitochondria were isolated in osmoregulatory organs. It was found that when the concentration of phosphorus increases to 0.3-0.6 mg/L in the water environment, it accumulates in the liver, and gills and decreases in the kidneys, while the concentration is maintained at a relatively constant level in the blood serum of fish. An increase in the concentration of inorganic phosphorus in the water environment significantly affects the processes of energy generation in the mitochondria of fish livers. A change in the phosphorus content in the habitat of fish has a significant impact on tissue bioenergetic processes, which is manifested in a change in the adenosine triphosphate content, and alkaline phosphatase activity. Significant changes in these indicators are observed in the gills and kidneys. The research results can be used for the formation of adaptive and compensatory regulatory mechanisms in the organism of hydrobionts during their adaptation to certain conditions of cultivation and reproduction
Keep Learning: Student Engagement in an Online Environment

Student engagement is a key factor in promoting learning and academic achievement. This study explores the factors underlying student engagement and the best practices advocated by students and faculty to engage students. Results revealed that student motivation to learn and self-efficacy are positively associated with student engagement. In addition, self-efficacy partially mediated the relationship between motivation to learn and student engagement. Finally, both faculty and students suggested diverse and inclusive techniques to engage students. Online education may become our new reality, and adjustment to this new world requires shifting to a new pedagogical paradigm.
Effects of Silicon Application and Groundwater Level in a Subirrigation System on Yield of a Three-Cut Meadow

The increasing demand for food and animal products makes it important to ensure that animals have sufficient fodder obtained from grassland. Unfortunately, there has been a recent decline in grassland areas, which makes it essential to find solutions to increase the grassland’s productivity and the quality of the fodder it yields. One of these solutions may be the use of appropriate irrigation and fertilization. The present study investigated the effect of the foliar application of silicon fertilizer and the groundwater level in a subirrigation system on the yield of a three-cut meadow. Four different experimental plots were used: high groundwater level (HWL), high groundwater level with silicon application (HWL_Si), lower groundwater level (LWL), and lower groundwater level with silicon application (LWL_Si). The analyses showed that silicon significantly reduced the amount of dry matter obtained in each of the three meadow cuts during the year. Furthermore, the plot with a higher groundwater level had an annual yield of 12.69 Mg·ha−1, whereas when silicon was applied to this area, it was 10.43 Mg·ha−1 (17.8% reduction in dry matter). A similar trend was noted at lower water levels, in which silicon also caused a dry matter reduction. However, the experiment did not indicate a statistically significant effect of silicon application on plant height and NDVI values. These results show that further research is still needed to better understand silicon’s effect on meadow sward.
Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina

Early crop disease detection is one of the most important tasks in plant protection. The purpose of this work was to evaluate the early wheat leaf rust detection possibility using hyperspectral remote sensing. The first task of the study was to choose tools for processing and analyze hyperspectral remote sensing data. The second task was to analyze the wheat leaf biochemical profile by chromatographic and spectrophotometric methods. The third task was to discuss a possible relationship between hyperspectral remote sensing data and the results from the wheat leaves, biochemical profile analysis. The work used an interdisciplinary approach, including hyperspectral remote sensing and data processing methods, as well as spectrophotometric and chromatographic methods. As a result, (1) the VIS-NIR spectrometry data analysis showed a high correlation with the hyperspectral remote sensing data; (2) the most important wavebands for disease identification were revealed (502, 466, 598, 718, 534, 766, 694, 650, 866, 602, 858 nm). An early disease detection accuracy of 97–100% was achieved from fourth dai (day/s after inoculation) using SVM.
Impact of Cluster Front Line Demonstrations on Productivity and Profitability of Pigeonpea under NFSM in Beed District of Maharashtra, India

Background: Pulses are the integral part of many diets across the globe and they have great potential to improve human health, conserve our soils, protect the environment and contribute to global food security. India is the largest producer, consumer and importer of the pulses in the world. Pigeonpea is an important pulse crop that plays an important role in the food and nutritional security of people in the developing countries and contributing to protein intake. It is a soil building crop and being a leguminous crop, fixes atmospheric nitrogen through symbiotic fixation, there by helps in N cycling within the ecosystem. Method: Cluster frontline demonstrations on BDN 711 variety of pigeonpea were conducted bsy Krishi Vigyan Kendra, Khamgaon of Beed District, Maharashtra State. Cluster frontline demonstration of pigeonpea variety BDN 711 were conducted during 2019-20 to 2022-23 on 80 ha area in 200 number of total demonstrations. Pigeonpea variety BDN 711 released by Vasantarao Naik Marathada Krishi Vidyapeeth is resistant to wilt, drought tolerant and suitable for medium type of soils. Improved cultivation practices were demonstrated at farmer’s field for four consecutive years by active participation of farmers with an objective of improved technologies of pigeonpea production potentials. Results: The result of the demonstrations concluded that an average yield of pigeonpea under improved technology ranged from 1430 kg/ha to 1800 kg/ha with a mean 1664 kg/ha; which was 30.61 per cent higher over farmers practice (1274 kg/ha). Morever, average yield recorded was higher under demonstrations over district and state average. The study exhibited mean extension gap of 391 kg/ha, mean technology gap 535 kg/ha with mean technology index 25.23 per cent. Higher mean net return of Rs. 71912/ha with mean B:C ratio 2.36 was obtained with improved technologies in comparison to farmers practice. The results clearly indicated that the beneficial impact of front line demonstrations over the farmers practices towards enhancing the productivity of pigeonpea cultivation in Beed district of Maharashtra state, India. Demonstrated technologies proved more remunerative and economically viable compared to farmers traditional practices in pigeonpea cultivation.
Islamic Philanthropy and Optimization of ZISWAF as a Solution Overcoming the Economic Recession

This study focuses on the Islamic philanthropy movement and the optimization of ZISWAF conducted by LAZIS Sabilillah Malang City in an effort to overcome the economic recession in the city of Malang. Islamic philanthropy is an effort to alleviate poverty and empower the mustahiq economy through the provision of capital loans and business assistance. The purpose of this study is to analyze the effect of Islamic philanthropy variables and the optimization of ZIWAF on efforts to overcome the economic recession in Malang City through economic empowerment of business development and its implications for mustahiq through the utilization of zakat, infaq and shadaqah funds in LAZIS Sabilillah Malang City. The research methodology used in this research is research quantitative analysis tools with Smart PLS which aims to build a detailed view of the object of research by collecting information or symptoms that are happening now and then looking for solutions in solving the problem with a quantitative approach. The research design itself consists of two main activities, namely the data collection design and the sampling design. This study aims to determine the role of Islamic philanthropy that affects the optimization of ZISWAF against the economic recession in Malang City.From the results of this research, it can be found that the optimization of ZISWAFable to mediate the relationship of Islamic philanthropy programs carried out on the economic recession variable. Because the direct relationship between Islamic philanthropy and economic recession is significant, the optimization of ZISWAF mediates the relationship between Islamic philanthropy and partial mediation. The higher the ZISWAF optimization value obtained by mustahiq, the higher the success of overcoming the economic recession. the higher the Islamic philanthropy that exists and is obtained from mustahiq, the higher the success rate of overcoming the economic recession that is carried out. The higher the ZISWAF optimization obtained by mustahiq, the higher the success rate in overcoming the economic recession that is carried out.